Note: Descriptions are shown in the official language in which they were submitted.
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MEDICAL DECISION SUPPORT SYSTEM
CROSS-REFERENCE TO RELATED APPLICATIONS
The instant application claims benefit of the following: U.S. Provisional
Application
Serial No. 63/061,770 filed on 05 August 2020, and U.S. Provisional
Application Serial No.
63/062,424 filed on 06 August 2020, each of which is incorporated herein by
reference in its
entirety.
BRIEF DESCRIPTION OF THE DRAWINGS
In the accompanying drawings:
FIG. 1 illustrates a block diagram of a coronary-artery-disease detection
system;
to FIG. 2 illustrates a first aspect of a data recording module and a
first aspect of an
associated docking system, in accordance with a first aspect of the coronary-
artery-disease
detection system illustrated in FIG. 1;
FIG. 3 illustrates a fragmentary view of a human thorax and associated
prospective
locations of auscultatory sound sensors at associated right, sternum and left,
second, third, fourth
and fifth, inter-costal spaces, left posterior locations at the second and
third inter-costal spaces,
and locations proximate to the heart apex;
FIG. 4 illustrates a second aspect of a data recording module and a second
aspect of an
associated docking system, in accordance with a second aspect of the coronary-
artery-disease
detection system illustrated in FIG. 1;
FIG. 5a illustrates an auscultatory sound sensor coupled to the skin of a test-
subject, by
bonding via associated adhesive layers or surfaces on both sides of an
adhesive interface;
FIGS. 5b and 5c each illustrate an auscultatory sound sensor that is detached,
and
therefore fully decoupled, from the skin of a test-subject, wherein FIG. 5b
illustrates the
associated adhesive interface detached from the skin of the test-subject, and
FIG. 5c illustrates
the associated adhesive interface detached from the auscultatory sound sensor;
FIGS. 5d through 5g each illustrate an auscultatory sound sensor that is
partially
coupled to, but debonded from, the skin of a test-subject;
FIG. 6 illustrates a test-subject reclined on a surface, with their torso
inclined while
capturing auscultatory sound signals from a plurality of auscultatory sound
sensors attached to
the thorax of the test-subject;
FIG. 7 illustrates a flowchart of a first aspect of an associated auscultatory-
sound-sensing
process that incorporates a process for detecting a decoupling of the
associated auscultatory
sound sensors from the skin of the thorax of a test-subject being diagnosed
for a prospective
abnormal cardiovascular condition, wherein the decoupling-detection process
occurs after each
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block of breath-held auscultatory sound time-series data is acquired, and is
based upon scaled
time-series data and responsive to an associated pre-determined debond-
detection threshold;
FIG. 8 illustrates a flowchart of a first aspect of a process for acquiring
auscultatory
sound signals from the associated auscultatory sound sensors coupled to the
skin of the thorax of
the test-subject being diagnosed for a prospective abnormal cardiovascular
condition;
FIG. 9 illustrates a plurality of six blocks of breath-held, auscultatory-
sound-sensor time-
series data recorded from an auscultatory sound sensor coupled to the skin of
the thorax of a test-
subject being diagnosed for a prospective abnormal cardiovascular condition;
FIGS. 10a-10f respectively illustrate a simulation of successively recorded
blocks of
breath-held, sensor time-series data illustrated in FIG. 9, each illustrated
with an expanded time
scale, wherein FIGS. 10a-10e illustrates a condition for which the
auscultatory sound sensor is
coupled to the skin of the test-subject, and FIG. 10f illustrates a condition
for which the
auscultatory sound sensor is decoupled from the skin of the test-subject;
FIG. 11 illustrates a flowchart of a process for determining a scale factor
used to scale
auscultatory-sound-sensor time-series data, the latter of which is analyzed to
detect whether or
not the associated auscultatory sound sensor is decoupled from the skin of the
test-subject,
wherein the scale factor provides for directly determining if the associated
auscultatory sound
sensor is detached from the skin of the test-subject;
FIGS. 12a-12f respectively illustrate time-series of the absolute values of
the
corresponding time-series data illustrated in FIGS. 10a-10f, further
illustrating a division of the
block of breath-held, sensor time-series data into a plurality of associated
data segments, with
each data segment of sufficient width to nominally include sound from a single
heartbeat, and
with the peak values in each data segment marked, wherein FIGS. 12a-12e
illustrates a
condition for which the auscultatory sound sensor is coupled to the skin of
the test-subject, and
FIG. 12f illustrates a condition for which the auscultatory sound sensor is
decoupled from the
skin of the test-subject;
FIG. 13 illustrates an accelerometer on the thorax of a test-subject during a
respiration
cycle of the test-subject;
FIG. 14 illustrates a breath-hold detection process:
FIG. 15 illustrates a flowchart of a first aspect of a process for detecting
whether or not
an auscultatory sound sensor is debonded from the skin of a test-subject;
FIG. 16 illustrates an organization of data from an auscultatory sound sensor
recorded by
an auscultatory coronary-artery-disease detection system from a test-subject;
FIG. 17 illustrates a flowchart of a noise detection process;
FIG. 18 illustrates a flowchart of a process for generating a matched noise
filter;
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FIG. 19 illustrates a flowchart of a process for evaluating the noise content
in a spectral
signal of an auscultatory sound signal;
FIG. 20 illustrates a flowchart of a process for logging results from the
noise-evaluation
process of FIG. 19;
FIG. 21 illustrates a block diagram of a process for preprocessing and
screening
auscultatory sound signals;
FIG. 22a illustrates a process for pre-processing auscultatory sound signals
from
auscultatory sound-or-vibration sensors;
FIG. 22b illustrates a process for pre-processing electrographic signals from
an ECG
to sensor;
FIG. 23 illustrates a process for segmenting auscultatory sound signals from
auscultatory
sound-or-vibration sensors, by heart cycle based upon an electrographic signal
from an ECG
sensor, and by heat phase based upon the auscultatory sound signals;
FIG. 24 illustrates an auscultatory sound signal from an auscultatory sound-or-
vibration
sensor;
FIG. 25 illustrates a corresponding electrographic signal from an ECG sensor,
in
correspondence with the auscultatory sound signal illustrated in FIG. 24:
FIG. 26 illustrates a process for identifying heart-cycle boundaries in an
electrographic
signal from an ECG sensor;
FIG. 27 illustrates a first process for generating an envelope of a signal,
which is called
from the process illustrated in FIG. 26;
FIG. 28 illustrates an envelope, and associated peaks, of a portion of the
electrographic
signal illustrated in FIG. 25, generated in accordance with the process
illustrated in FIGS. 26
and 27;
FIG. 29 illustrates a first aspect of a process for validating the peaks of an
electrographic
signal, which is called from the process illustrated in FIG. 26;
FIG. 30 illustrates a plot of the auscultatory sound signal illustrated in
FIG. 24 together
with plot of an envelope of the corresponding electrographic signal
illustrated in FIGS. 25 and
28;
FIG. 31 illustrates a plot of the auscultatory sound signal illustrated in
FIG. 24, but over
a relatively greater total period of time, with the auscultatory sound signal
segmented based upon
the peaks of the envelope of the associated electrographic signal, and
presented as a beat stack,
each illustrating a corresponding associated heart beat;
FIG. 32 illustrates a second process for generating an envelope of a signal,
which is
called from the process illustrated in FIG. 23;
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FIG. 33 illustrates a rectified auscultatory sound signal and an associate
envelope thereof
determined in accordance with the process illustrated in FIG. 32;
FIG. 34 illustrates a plot of the auscultatory sound signal that is shown
rectified in FIG.
33, together with plots of the associated quadratic models of the associated
envelopes in
proximity to the associated S/ and S2 heart sounds, illustrating time-points
associated with zero-
amplitude roots of the associated quadratic models that are used to locate
associated heart
phases;
FIG. 35 illustrates the auscultatory sound signal beat stack illustrated in
FIG. 31,
together with indicia showing the locations of the roots of the quadratic
models associated with
to
the Si and S2 heart sounds that are used to locate associated heart phases,
with the auscultatory
sound signals of the beat stack aligned with one another based upon the mean
values of the roots
of the S2 heart sounds;
FIG. 36 illustrates a process for identifying outliers in a diastole region of
an auscultatory
sound signal;
FIG. 37 illustrates a flow chart of a process for selecting valid heart cycles
and analyzing
the results therefrom;
FIG. 38 illustrates a portion an auscultatory sound signal during diastole,
and also
illustrates associated noise power thresholds;
FIG. 39a illustrates an auscultatory sound signal for a plurality of heart
cycles;
FIG. 39b illustrates the auscultatory sound signal for the plurality of heart
cycles
illustrated in FIG. 39a, with the heart cycles aligned with respect to the
mean times of the S2
heart sound;
FIG. 40a illustrates the auscultatory sound signal for a plurality of heart
cycles illustrated
in FIG. 39b, including indications of the start of the S2 heart sound and the
end of diastole;
FIG. 40b illustrates portions of the auscultatory sound signal during diastole
for the
plurality of heart cycles illustrated in FIG. 40a, with the heart cycles
temporally normalized and
resampled;
FIG. 41 illustrates a cross-correlation process;
FIG. 42 illustrates a image of a plurality of localized cross-correlation
signals associated
with auscultatory sound signals from a plurality of heart cycles;
FIG. 43 illustrates a time-frequency analysis based on a continuous wavelet
transform of
an auscultatory sound signal during diastole;
FIG. 44 illustrates a process for detecting coronary artery disease from
features of cross-
correlation images generated from analysis of auscultatory sound signals
during diastole for a
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plurality of heart cycles, incorporating a Convolution Neural Network (CNN)
classifier with a
single convolution layer;
FIG. 45 illustrates a display of results in a heart/arteries view and textual
view;
FIG. 46a illustrates a displace of a Receiver Operating Characteristic (ROC)
curve;
FIG. 46b illustrates line graph display of true positives, true negatives,
false negatives,
and false positives;
FIG. 47 illustrates a display of a Heartbeat View comprising a graphical plot
of the
systolic and diastolic intervals of each heartbeat captured;
FIG. 48 illustrated a display of a Stacked Heartbeat View;
to FIG. 49 illustrates a Bruit Identification View in a Line Graphs with
Underfill mode,
FIG. 50 illustrates a display of a Bruit Identification View in a Spectrogram
mode;
FIG. 51 illustrates a display of a Bruit Analysis View;
FIG. 52 illustrates a comparison of a current test with a previous test, using
the Bruit
Identification View, Spectrogram mode, to confirm success of PCI;
FIG. 53 illustrates a comparison of a current test with a previous test, using
the Bruit
Identification View, Line Graph with Underfill mode, to confirm success of
PCI;
FIG. 54 illustrates a second aspect of a process for detecting R-peaks in an
electrographic
signal;
FIG. 55 illustrates an envelope, and associated peaks, of a portion of the
electrographic
signal that includes noise and other signal components to be ignored;
FIG. 56 illustrates a second aspect of a process for validating the peaks of
an
electrographic signal, which is called from the process illustrated in FIG.
26, following the
second aspect process for detecting R-peaks in an electrographic signal,
illustrated in FIG. 26;
FIG. 57 illustrates a three-level Discrete Wavelet Transformation (DWT)
process;
FIG. 58 illustrates the effective filter spectra associated with the outputs
of the three-
level Discrete Wavelet Transformation process illustrated in FIG. 57;
FIG. 59 illustrates a scaling function of a Daubechies 4 (db4) wavelet family;
FIG. 60 illustrates a wavelet function of a Daubechies 4 (db4) wavelet family;
FIG. 61 illustrates a kernel discrete wavelet transformation process;
FIG. 62 illustrates a three-level Wavelet Packet Transformation (WPT) process;
FIG. 63 illustrates the effective filter spectra associated with the outputs
of the three-
level Discrete Wavelet Transformation process illustrated in FIG. 62;
FIG. 64 illustrates a high-pass filtered auscultatory sound signal for a
single heart cycle
from a high-pass filter having a 30 Hz cut-off frequency;
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FIG. 65 illustrates a Wavelet Packet Transformation energy map for the high-
pass
filtered auscultatory sound signal illustrated in FIG. 64, with either
decomposition levels;
FIG. 66 illustrates a selected basis of frequency bins from the Wavelet Packet
Transformation energy map illustrated in FIG. 64, selected using Shannon
entropy as a cost
function:
FIG. 67 illustrates a Support Vector Machine (SVM) classifier trained to
distinguish two-
class data samples with respect to an associated highest-margin hyperplane;
FIG. 68 illustrates a feed-forward artificial neural network for binary
classification;
FIG. 69 illustrates a geometric construction that provides for determining a
projected
to transverse location of an acoustic source with respect to a plane
containing three auscultatory
sound sensors;
FIG. 70 illustrates various phases of a heart cycle in relation to an
electrographic signal;
FIG. 71 illustrates the Si, S2, S3 and S4 phases of a heart cycle in relation
to an
electrographic signal;
FIG. 72 illustrates a second embodiment of a Convolution Neural Network (CNN);
FIG. 73 illustrates a stack of heartbeats prior to processing by a third-
aspect quality
assessment procedure;
FIG. 74 illustrates a third aspect of a quality assessment process that
provides for
assessing the quality of an auscultatory sound signal;
FIG. 75 illustrates a process for pruning an auscultatory sound signal of
heart cycles that
do not satisfy one or more associated quality assessment tests, in association
with the third aspect
of a quality assessment process illustrated in FIG. 74;
FIG. 76 illustrates a stack of heartbeats following processing by the third-
aspect quality
assessment procedure, corresponding to the stack of heartbeats illustrated in
FIG. 73;
FIG. 77 illustrates an auscultatory sound signal associated with heart sounds
of a single
heart cycle, further illustrating locations of associated Si, S2, S3, S4 and
S5 sounds, the Weissler
point used as a basis for identifying an associate region of diastasis, and
associated intervals;
FIG. 78 illustrates an S3-detection-training process for training a third
heart sound (S3)
detection process based upon a training set of a plurality of test-subjects,
so as to provide for an
unsupervised classification of auscultatory sound signals into two different
classes that provide
for the detection of a third heart sound (53) in an auscultatory sound signal;
FIG. 79 illustrates a flow chart of a k-means-clustering training process that
is called
both from the process illustrated in FIG. 78 for exclusively a training set of
test-subjects, and
from the third heart sound (S3) detection process illustrated in FIG. 83 for a
different set of test-
subjects, and that provides for processing a plurality of test-subjects to
determine associated
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features from associated auscultatory sound signals to be used in an
associated k-means
clustering unsupervised classification process, and for then clustering the
heartbeats from the set
of test-subjects into two clusters using the k-means clustering process;
FIG. 80 illustrates an S3-clustering feature determination process that is
called from the
process illustrated in FIG. 79, and that provides for determining, from an
auscultatory sound
signal of a particular test-subject, the associated features to be used in the
k-means clustering
unsupervised classification process called from the process illustrated in
FIG. 79;
FIG. 81a illustrates an ECG signal associated with a single heart cycle;
FIG. 81b illustrates a non-filtered auscultatory sound signal associated with
a heart
to
sound associated with the same single heart cycle associated with the ECG
signal illustrated in
FIG. 81a
FIG. 81c illustrates a band-pass-filtered version of the auscultatory sound
signal
illustrated in FIG. 81b, for purposes of illustrating the process illustrated
in FIGS. 79, 80 and 83
for detecting the presence of a third heart sound;
FIG. 81d illustrates the results of Short Time Fourier Transform (STFT) of the
band-
pass-filtered auscultatory sound signal illustrated in FIG. 81c, for purposes
of illustrating the
process illustrated in FIGS. 79, 80 and 83 for detecting the presence of a
third heart sound;
FIG. 82 illustrates the outcome of a k-Means clustering process used by the
process
illustrated in FIGS. 79, 80 and 83 for detecting the presence of a third heart
sound;
FIG. 83 illustrates a flow chart of a process for detecting the presence of a
third heart
sound (S3) in an auscultatory sound signal;
FIG. 84 illustrates a flow chart of a process for determining a mean value of
a S4S2swiNG
metric from a training set of test-subjects, wherein the mean value of the
S4S2swiNG metric is
used by the process illustrated in FIG. 87 for detecting the presences of a
fourth heart sound in
an auscultatory sound signal;
FIG. 85 illustrates a flow chart of a process for determining an array of
values of the
S4S2swiNG metric used in the processes illustrated in FIGS. 84 and 87, the
latter of which
provides for detecting the presences of a fourth heart sound in an
auscultatory sound signal;
FIG. 86a illustrates an ECG signal associated with a single heart cycle;
FIG. 86b illustrates band-pass-filtered auscultatory sound signal for purposes
of
illustrating the process illustrated in FIG. 85 for determining an array of
values of the S4S2swING
metric;
FIG. 87 illustrates a flow chart of a process for detecting the presence of a
fourth heart
sound in an auscultatory sound signal;
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FIG. 88 illustrates a flow chart of a process for training a Support Vector
Machine
(SVM) to detect coronary artery disease (CAD) responsive to swing measure data
associated
with a test-subject;
FIG. 89 illustrates a flow chart of a process for determining a plurality of
swing
measures for a plurality of frequency bands from auscultatory sound signals
from one or more
auscultatory sound sensors operatively associated with a given test-subject,
called from the
processes illustrated in FIGS. 88 and 93;
FIG. 90 illustrates a flow chart of a process for determining a plurality of
swing
measures from auscultatory sound signals from an auscultatory sound sensors
operatively
to associated with a given test-subject, called from the process
illustrated in FIG. 89;
FIG. 91 illustrates a flow chart of a process for optionally determining a
plurality of
supplemental swing measures from the swing measures determined in from the
process
illustrated in FIG. 90, called from the process illustrated in FIG. 90
FIG. 92 illustrates flow chart of a process for optionally determining median
values of
the plurality of supplemental swing measures determined in from the process
illustrated in FIG.
90, called from the process illustrated in FIG. 90;
FIG. 93 illustrates flow chart of a process for detecting whether or not an
auscultatory
sound signal of a test-subject is associated with a condition of coronary
artery disease using the
Support Vector Machine (SVM) that is trained by the process illustrated in
FIG. 88, and that is
responsive to swing-measure data determined from an auscultatory sound signal
of the test-
subject;
FIG. 94 illustrates a flowchart of a process for training a Support Vector
Machine (SVM)
to be used for detecting whether or not an auscultatory sound signal of a test-
subject is associated
with a condition of coronary artery disease, based upon energy within a
plurality of time and
frequency intervals of the auscultatory sound signal;
FIG. 95 illustrates a CAD-detection feature determination process that
provides for
determining a feature set for use by the Support Vector Machine (SVM)
associated with the
processes of each of FIGS. 94 and 96, responsive to an auscultatory sound
signal of an
associated test-subj ect; and
FIG. 96 illustrates a flow chart of a process for detecting whether or not an
auscultatory
sound signal of a test-subject is associated with a condition of coronary
artery disease, based
upon energy within a plurality of time and frequency intervals of the
auscultatory sound signal,
using the Support Vector Machine (SVM) that was trained in accordance with the
associated
training process illustrated in FIG.94.
DESCRIPTION OF EMBODIMENT(S)
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Referring to FIGS. 1 and 2, an auscultatory coronary-artery-disease detection
system
incorporates at least one auscultatory sound sensor 12 that is operatively
coupled to a
recording module 13 running at least a first portion 14.1 of a Data Recording
Application
(DRA) 14, 14.1 on a first specialized computer or electronic system comprising
a first
5 computer processor or FPGA (Field Programmable Gate Array) 15 and first
memory 17
powered by a first power supply 19, which provides for recording and
preprocessing
auscultatory sound signals 16 from the at least one auscultatory sound sensor
12. For
example, in one set of embodiments, the at least one auscultatory sound sensor
12 comprises a
first group 12' of three auscultatory sound sensors 12, 12r, 122', 123'
physically
10 interconnected end-to-end with one another, and physically and
electrically interconnected with a
first wireless transceiver 18; and a second group 12" of three auscultatory
sound sensors 12,
12r', 122", 123" physically interconnected end-to-end with one another, and
physically and
electrically interconnected with the first wireless transceiver 18, with both
groups 12', 12" of
auscultatory sound sensors placed on the skin of the thorax 20 of a test-
subject 22, in acoustic
communication therewith. Referring also to FIG. 3, the placement of the first
group of
auscultatory sound sensors 12' in FIG. 1 is illustrated with the respective
associated
auscultatory sound sensors 12, 121', 122', 123' in substantial alignment with
the corresponding
respective third R3, fourth R4 and fifth R5, inter-costal spaces on the right
side 2011 of the
thorax 20, and the placement of the second group of auscultatory sound sensors
12" in FIG.
1 is illustrated with the respective associated auscultatory sound sensors 12,
121", 122", 123" in
substantial alignment with the corresponding respective third L3, fourth L4
and fifth L5,
inter-costal spaces on the left side 201' of the thorax 20. Prospective sensor
locations R2-R5,
S2-SS, and L2-L.5 illustrated in FIG. 3 respectively refer to the second R2
through fifth R5
inter-costal spaces on the right side 20R of the thorax 20, the second S2
through fifth S5
inter-costal spaces at the center/sternum 20s of the thorax 20, and the second
L2 through
fifth L5 inter-costal spaces on the left side 201' of the thorax 20.
Furthermore, prospective left-
side posterior sensor locations LP2 and LP3 illustrated in FIG. 3 respectively
refer to at the
second LP2 and third LP3 intercostal spaces locations on the posterior of the
thorax 20. Yet
further, prospective sensor locations HA-1 and HA-2 are proximate to the apex
of the heart,
either on the anterior or the posterior of the thorax 20. For example, in one
set of embodiments,
The auscultatory sound sensors 12, 121', 122', 123", 121", 122", 123" are
located at the second
S2, L2, third S3, L3 and fourth S4, L4 inter-costal spaces at the sternum S2-
S4 and left-side
L2-L4 of the thorax 20.
As used herein, the term -auscultatory sound- is intended to mean a sound
originating
from inside a human or animal organism as a result of the biological
functioning thereof, for
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example, as might be generated by action of the heart, lungs, other organs, or
the associated
vascular system; and is not intended to be limited to a particular range of
frequencies -- for
example, not limited to a range of frequencies or sound intensities that would
be audible to a
human ear, -- but could include frequencies above, below, and in the audible
range, and sound
intensities that are too faint to be audible to a human ear. Furthermore, the
term -auscultatory-
sound sensor" is intended to mean a sound sensor that provides for transducing
auscultatory
sounds into a corresponding electrical or optical signal that can be
subsequently processed.
The auscultatory sound sensors 12, 121', 122', 123' , 121", 122", 123" provide
for
transducing the associated sounds received thereby into corresponding
auscultatory sound
signals 16 that are preprocessed and recorded by an associated hardware-based
signal
conditioning/preprocessing and recording subsystem 25, then communicated to
the first
wireless transceiver 18, and then wirelessly transmitted thereby to an
associated second
wireless transceiver 26 of an associated wireless interface 26' of an
associated docking system
27, possibly running a second portion 14.2 of the Data Recording Application
(DRA) 14, 14.2
on a corresponding second specialized computer or electronic system comprising
an associated
second computer processor or FPGA (Field Programmable Gate Array) 28 and
second
memory 30, both of which are powered by an associated second power supply 32,
which
together provide for recording and preprocessing the associated auscultatory
sound signals 16
from the auscultatory sound sensors 12, 121', 122', 12', 121", 122", 123".
For example, in accordance with one set of embodiments, the hardware-based
signal
conditioning/preprocessing and recording subsystem 25 includes an amplifier --
either of
fixed or programmable gain, -- a filter and an analog-to-digital converter
(ADC). For example,
in one set of embodiments, the analog-to-digital converter (ADC) is a /6-bit
analog-to-digital
converter (ADC) that converts a -2.25 to +2.25 volt input to a corresponding
digital value of -
32, 768 to + 32, 767. Furthermore, in accordance with one set of embodiments
of the amplifier,
the amplifier gain is programmable to one of sixteen different levels
respectively identified as
levels 0 to 15, with corresponding, respective gain values of 88, 249, 411,
571, 733, 894, 1055,
1216, 1382, 1543, 1705, 1865, 2027, 2188, 2350 and 2510, respectively for one
set of
embodiments. In accordance with another set of embodiments of the amplifier,
the amplifier
gain is fixed at the lowest above value, i.e., for this example, 88, so as to
provide for avoiding the
relative degradation of the associated signal-to-noise ratio (SNR) that
naturally occurs with the
relatively high gain levels of the programmable-gain set of embodiments.
It should be understood that the associated processes of the Data Recording
Application
(DRA) 14 could be implemented either in software-controlled hardware,
hardware, or a
combination of the two.
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For example, in one set of embodiments, either or both the recording module 13
or
docking system 27 may be constructed and operated in accordance with the
disclosure of U.S.
Provisional Application No. 62/575,364 filed on 20 October 2017, entitled
CORONARY
ARTERY DISEASE DETECTION SYSTEM, or International Application No.
PCT/US2018/056832 filed on 22 October 2018, entitled CORONARY ARTERY DISEASE
DETECTION SIGNAL PROCESSING SYSTEM AND METHOD, each of which is
incorporated by reference in its entirety.
Furthermore, in accordance with one set of
embodiments, the auscultatory coronary-artery-disease detection system 10 may
further
incorporate an ECG sensor 34, for example, in one set of embodiments, an ECG
sensor 34'
to comprising a pair of electrodes incorporated in a corresponding
pair of auscultatory sound
sensors 12, wherein the signal from the ECG sensor 34' is also preprocessed
and recorded by a
corresponding different signal channel of the same hardware-based signal
conditioning/preprocessing and recording subsystem 25 of the recording module
13 that is
used to preprocess the signals from the one or more auscultatory sound sensors
12.
Alternatively, the ECG sensor 34 may comprise a separate set of a pair or
plurality of electrodes
that are coupled to the skin of the test-subject, for example, in one set of
embodiments, a pair of
signal electrodes 35, 35 /- in cooperation with a ground electrode 350,
wherein, referring to
FIG. 3 (illustrating the locations of the electrodes 35, 35+/-, 35 ), in
accordance with one non-
limiting embodiment, the signal electrodes 35, 35+/- span the heart from
diametrically-opposed
quadrants of the torso 44, and the ground electrode 350 is located in a
different quadrant,
orthogonally displaced from a midpoint of a baseline connecting the signal
electrodes 35, 35-"-.
Alternatively the signal electrodes 35, 35+1- could span across the top of the
top of the thorax
20, or at any pair of locations commonly used for conventional ECG tests.
Furthermore, in one
set of embodiments, the recording module 13 and docking system 27 may each
incorporate a
corresponding respective USB interface 36.1, 36.2 to provide for transferring
corresponding
auscultatory sound signals 16 and or an electrographic signal 37 ¨ from an
associated ECG
sensor 34, 34' -- from the recording module 13 to the docking system 27, for
example, rather
than relying upon the first 18 and second 26 wireless transceivers of an
associated wireless
interface 26'. Further alternatively, either instead of, or in addition to,
the wireless interface
26' or the USB interface 36.1, 36.2, data may be transferred from the
recording module 13 to
the docking system 27 via a portable memory element, for example, either an SD
memory card
or a Micro SD memory card.
The functionality of the Data Recording Application (DRA) 14 is distributed
across the
recording module 13 and the docking system 27. For example, referring to FIG.
2, in
accordance with a first aspect 10' of an auscultatory coronary-artery-disease
detection
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system 10, 10', the Data Recording Application (DRA) 14 spans across the
recording module
13 and the docking system 27, with a first portion 14.1 comprising the
hardware-based signal
conditioning/preprocessing and recording subsystem 25 operative on the
recording module
13, and a remaining second portion 14.2 operative on the docking system 27.
Alternatively, as
another example, referring to FIG. 4, in accordance with a second aspect 10"
of an
auscultatory coronary-artery-disease detection system 10, 10", the Data
Recording
Application (DRA) 14 is operative entirely on the recording module 13.
The auscultatory sound sensor 12 provides for sensing sound signals that
emanate from
within the thorax 20 of the test-subject 22 responsive to the operation of the
test-subject's heart,
to and the resulting flow of blood through the arteries and veins, wherein
an associated build-up of
deposits therevvithin can cause turbulence in the associated blood flow that
can generate
associated cardiovascular-condition-specific sounds, the latter of which can
be detected by a
sufficiently-sensitive auscultatory sound sensor 12 that is acoustically
coupled to the skin 38 of
the thorax 20 of the test-subject 22. For some cardiovascular-conditions
associated with, or
predictive of, a cardiovascular disease, the sound level of these
cardiovascular-condition-specific
sounds can be below a level that would be detectable by a human using a
conventional
stethoscope. However, these sound levels are susceptible to detection by
sufficiently sensitive
auscultatory sound sensor 12 that is sufficiently acoustically coupled to the
skin 38 of the
thorax 20 of the test-subject 22. For example, in one of embodiments, the
auscultatory sound
sensor 12 may be constructed in accordance with the teachings of U.S.
Provisional Application
No. 62/568,155 filed on 04 October 2017, entitled AUSCULTATORY SOUND SENSOR,
or
International Application No. PCT/US2018/054471 filed on 04 October 2018,
entitled
AUSCULTATORY SOUND-OR-VIBRATION SENSOR, each of which is incorporated by
reference in its entirety. Furthermore, in another set of embodiments, the
auscultatory sound
sensor 12 may be constructed in accordance with the teachings of U.S. Pat.
Nos. 6,050,950,
6,053,872 or 6,179,783, which are incorporated herein by reference.
Referring also to FIG. 5a, the auscultatory sound sensors 12, 121', 122', 123'
, 121",
122", 123" are acoustically coupled to the skin 38 of the thorax 20 of the
test-subject 22 via an
acoustic interface 40, for example, via a hydrogel layer 40', that also
functions as an associated
adhesive interface 42 that is attached to the associated auscultatory sound
sensor 12, 121',
122', 12', 121", 122", 123" with a first adhesive layer 42.1, for example,
either a first surface
40.1' of the hydrogel layer 40' or a first layer of double-sided tape 42.1' on
a first side of the
acoustic/adhesive interface 40, 42, and that is attached to the skin 38 of the
thorax 20 of the
test-subject 22 with a second adhesive layer 42.2, for example, either a
second surface 40.2'
of the hydrogel layer 40' or a second layer of double-sided tape 42.2' on the
opposing, second
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side of the acoustic/adhesive interface 40, 42. When fully coupled - as
illustrated in FIG. 5a, -
- the auscultatory sound sensor 12, 12Y, 122', 123' , 121", 122", 123" is
fully attached to the
acoustic/adhesive interface 40, 42 via the first adhesive layer 42.1, 42.1',
40.1', and the
acoustic/adhesive interface 40, 42 is fully attached to the skin 38 of the
thorax 20 of the test-
subject 22 via the second adhesive layer 42.2, 42.2', 40.2', so that sound
signals from within
the thorax 20 of the test-subject 22 can propagate otherwise unimpeded to the
auscultatory
sound sensor 12, 121', 122', 12", 121", 122", 123", thereby providing for a
maximum achievable
level of the corresponding associated auscultatory sound signals 16, and
thereby improving the
prospect of detecting an associated abnormal cardiovascular condition - if
present - therefrom.
to Referring to FIGS. 5b and 5c - with the acoustic/adhesive interface 40,
42 respectively
detached from the skin 38 or detached from the auscultatory sound sensor 12,
respectively, --
if the auscultatory sound sensor 12, 121', 122', 12", 121", 122', 123" is
completely detached
from the skin 38 of the thorax 20 of the test-subject 22, and thereby fully
decoupled therefrom,
the resulting auscultatory sound sensor 12, 121', 122', 123' , 121", 122",
123" would be
substantially non-responsive to sound signals from within the thorax 20 of the
test-subject 22.
Referring to FIGS. 5d to 5g, if the auscultatory sound sensor 12, 121', 122',
12', 121", 122",
123" is partially attached to the skin 38 of the thorax 20 of the test-subject
22, and thereby
partially decoupled therefrom - i.e., in a condition referred to herein as
being debonded
therefrom -- the resulting auscultatory sound sensor 12, 121', 122', 12",
122", 123" would
be only partially responsive to sound signals from within the thorax 20 of the
test-subject 22,
but not sufficiently responsive to provide for an associated auscultatory
sound signal 16 of
sufficient amplitude to provide for reliably detecting a prospective
associated abnormal
cardiovascular condition. More particularly, FIGS. 5d and 5e respectively
illustrate an
acoustic/adhesive interface 40, 42 partially detached from skin 38, and an
acoustic/adhesive
interface 40, 42 partially detached from an auscultatory sound sensor 12,
respectively.
Furthermore, FIG. 5f illustrates an auscultatory sound sensor 12 attached to a
wrinkled
acoustic/adhesive interface 40, 42, and FIG. 5g illustrates an
acoustic/adhesive interface 40,
42 attached to wrinkled skin 38. In anticipation of prospective problems with
nature of the
attachment of the acoustic/adhesive interface 40, 42, the Data Recording
Application (DRA)
14 is provided with a means - described more fully hereinbelow -- for
detecting if one or more
auscultatory sound sensors 12, 121', 122', 123' , 12y, 122", 123" is, or are,
either detached or
debonded from the skin 38 of the thorax 20 of the test-subject 22, so as to
provide for
excluding data from auscultatory sound sensors 12, 121', 122', 123' , 121",
122", 123" that are
either detached or debonded, from the skin 38 of the thorax 20 of the test-
subject 22 from
being used to diagnose a prospective abnormal cardiovascular condition.
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Generally, the adhesive interface 42 could comprise either a hydrogel layer
40', for
example, P-DERM Hydrogel, a silicone material, for example, a P-DERM
Silicone Gel
Adhesive; an acrylic material, for example, a P-DERM Acrylic Adhesive; a
rubber material; a
synthetic rubber material; a hydrocolloid material; or a double-sided tape,
for example, with
either rubber, acrylic or silicone adhesives.
Referring to FIG. 6, it has been found that the quality of the auscultatory
sounds acquired
from a test-subject 22 can be improved if the torso 44 of the test-subject 22
is inclined, For
example, in one set of embodiments, at an inclination angle 0 of about 30
degrees above
horizontal -- but generally, as close to upright (i.e. 0 = 90 degrees) as can
be accommodated by
u) the associated adhesive interface(s) 42 of the associated auscultatory
sound sensors 12, 121',
122', 12', 121", 122", 123" -- which imposes a transverse component of
gravitational force on
each of the auscultatory sound sensors 12, 121', 122', 123' , 121", 122", 123"
that is resisted by
the associated adhesive interface(s) 42.
Referring to FIGS. 7-15, in accordance with a first aspect 700, an
auscultatory-sound-
sensing process 700 provides for first determining a scale factor SF from an
initially-acquired
block of auscultatory-sound-sensor time-series data S, and initially
determining from the
scale factor SF whether one or more of the auscultatory sound sensors 12,
121', 122', 12',
121", 122", 123" is detached from the skin 38 of the thorax 20 of the test-
subject 22, wherein
when multiplied by the scale factor SF, the values of the associated
auscultatory-sound-sensor
time-series data S are nominally within a range that is a predetermined
percentage of the
dynamic range of the associated data acquisition system (for example, that
provides /6-bit
signed digital values). If there is no detachment, the first aspect 700, the
auscultatory-sound-
sensing process 700 provides for acquiring successive blocks of auscultatory-
sound-sensor
time-series data S while the test-subject 22 is holding their breath, and
determining from each
block of auscultatory-sound-sensor time-series data S - using an associated
predetermined
debond-detection threshold - whether or not one or more auscultatory sound
sensors 12, 121',
122', 12', 121", 122", 123" is debonded from the skin 38 of the thorax 20 of
the test-subject 22,
or whether there is excessive noise in the auscultatory-sound-sensor time-
series data S. The
auscultatory-sensor time-series data S is rejected if excessive noise is
detected, and the test is
aborted if one or more auscultatory sound sensors 12, 121', 122', 123' , 121",
122", 123" has
become decoupled from the skin 38 of the thorax 20.
More particularly, referring to FIG. 7, the first aspect 700 of the
auscultatory-sound-
sensing process 700 commences with step (702) by initializing a data-block
counter i to a
value of zero, and then, in step (704), acquiring a block of Ns contiguous
samples of
auscultatory-sound-sensor time-series data S in accordance with a first aspect
800 of a data
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acquisition process 800. This initially-acquired data is then used to
determine the scale factor
SF that is used to determine whether or not one or more auscultatory sound
sensors 12, 121'
,
122', 12', 121", 122", 123" is/are detached from the skin 38 of the thorax 20
of the test-subject
22, and then subsequently to scale subsequent blocks of time-series data S.
The initial block of
auscultatory-sound-sensor time-series data S may be acquired either with, or
without, the test-
subject 22 holding their breath, but typically with the test-subject 22
allowed to breath normally
-- for their comfort and convenience. The number of samples Ns to be acquired
is given by the
product of an acquisition period Si in seconds, times a sampling frequency Fs
in Hz. For
example, in one set of embodiments, in step (704), the initially-acquired
block of auscultatory-
sound-sensor time-series data S typically contains /0 seconds of data, which
at a sampling
frequency Fs of 24 KHz, results in Ns = 5i *Fs = 240,000 samples.
More particularly, referring to FIG. 8, the data acquisition process 800
commences
with step (802) by pre-filtering the electronic signal from the associated
auscultatory sound
sensor 12, 121', 122', 123' , 121", 122", 123" with an analog anti-aliasing
filter, for example, an
analog second-order band-pass filter having a pass band in the range of 3 Hz
to 2.5 KHz, for
which the upper-cutoff frequency is sufficiently below the sampling frequency
(i.e. no more than
half) so as to prevent high frequency components in the signal being sampled
from appearing as
low frequency components of the sampled signal, i.e. so as to prevent
aliasing. Following step
(802), if, in step (804), the test-subject 22 need not necessarily hold their
breath - as is the case
for the initially-acquired block of auscultatory-sound-sensor time-series data
S, -- then, in
step (806), the pre-filtered auscultatory sound signal 16 is sampled at the
sampling frequency
Fs and converted to digital form by the associated analog-to-digital (ADC)
converter. Then,
from step (808), the auscultatory sound signal 16 continues to be sampled in
step (806) until
Ns samples of the block of auscultatory-sound-sensor time-series data S have
been acquired,
after which, in step (810), the NS samples of auscultatory-sound-sensor time-
series data S are
returned to step (704) of the auscultatory-sound-sensing process 700. For
example, FIGS. 9
and 10a each illustrate a first block of auscultatory-sound-sensor time-series
data S of
duration 50 that was recorded from one of the auscultatory sound sensors 12,
121', 122', 12',
121", 122", 123".
Referring again to FIG. 7, following step (704), in step (706), the scale
factor SF is
determined from the initially-acquired block of auscultatory-sound-sensor time-
series data S,
in accordance with an associated scale-factor-determination process 1100. More
particularly,
referring to FIGS. 11 and 12a, the scale-factor-determination process 1100
commences with
step (1102), wherein the block of auscultatory-sound-sensor time-series data S
is divided into
a plurality of data segments 46, for example, each of the same data-segment
duration SD that
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nominally spans a single heartbeat, for example, about one second. For
example, in FIG. 12a,
the 50= 10 second block of auscultatory-sound-sensor time-series data S is
divided into Nseg =
data segments 46, each of SD =one second duration. FIG. 12a illustrates a time
series ISI
containing the absolute values of the auscultatory-sound-sensor time-series
data S illustrated
5 in FIG. 10a. As indicated by X's in FIG. 12a, for each data segment 46, m
spanning the range
k=kmiN(m) to k=kmAx(m) of the auscultatory-sound-sensor time-series data S(k),
the
corresponding maximum absolute value of the auscultatory-sound-sensor time-
series data
S(k) is determined, as given by:
= Max (IS (k)I) (1)
(m)
to Then, in step (1104), the median of these maximum values is determined,
as given by
median(Srx,<N, g)
(2)
Finally, in step (1106), the scale factor SF is determined, as given by:
SF = ,
(3)
median (Sr
wherein Dito_p is the nominal zero-to-peak dynamic range of the auscultatory-
sound-sensor
time-series data S after scaling, i.e. after multiplying the acquired values
by the scale factor SF.
For example, in one set of embodiments, the nominal zero-to-peak dynamic range
is set to be
about 80 percent ¨ more broadly, but not limiting, 80 percent plus or minus 15
percent -- of the
zero-to-peak range of the associated analog-to-digital converter -- for
example, in one set of
embodiments, a 16-bit signed analog-to-digital converter -- used to digitize
the auscultatory-
sound-sensor time-series data S in step (806). In one set of embodiments, the
scale factor SF
is integer-valued that, for an attached and bonded auscultatory sound sensor
12, 121', 122', 12',
121", 122", 123", ranges in value between 1 and 28.
If one or more of the associated auscultatory sound sensors 12, 121', 122',
12', 121",
122", 123" is detached from the skin 38 of the thorax 20 of the test-subject
22, then the
associated level of the auscultatory sound signals 16 will be low ¨ for
example, at a noise level
¨ resulting in a relatively large associated scale factor SF from step (1106).
Accordingly, if, in
step (1108), the scale factor SF is in excess of an associated threshold
SFmAx, then the Data
Recording Application (DRA) 14 is aborted in step (1110), and the operator 48
is alerted that
the one or more auscultatory sound sensors 12, 12Y, 122', 12', 121", 122-,
123" is/are detached,
so that this can be remedied. For example, in one set of embodiments, the
value of the threshold
SFmAx is 28 for the above-described fixed-gain embodiment, i.e. for which the
associated
amplifier has a fixed gain of 88, feeding a /6-bit analog-to-digital converter
(ADC) that provides
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for converting a +1- 5 volt input signal to +/- 32,767. Otherwise, from step
(1108), if the value
of the scale factor SF does not exceed the associated threshold SFmAx, in step
(1112), the scale
factor SF is returned to step (706) for use in scaling subsequently-recorded
breath-held
auscultatory sound signals 16.1.
Referring again to FIG. 7, following step (706), in step (708), the value of
the data-
block counter i is incremented, so as to point to the next block of
auscultatory-sound-sensor
time-series data S to be recorded. If, while this next block is recorded, the
auscultatory sound
sensors 12, 121', 122', 12', 121", 122-, 123" remain attached and bonded to
the skin 38 of the
thorax 20 of the test-subject 22, and the associated breath-held auscultatory
sound signals
16.1 are not corrupted by excessive noise, then this next block of
auscultatory-sound-sensor
time-series data S will then be subsequently used to detect a prospective
abnormal
cardiovascular condition therefrom. The auscultatory sound signals 16 used to
detect
prospective abnormal cardiovascular conditions are recorded while the test-
subject 22 holds
their breath, the latter to prevent the resulting cardiovascular-based
auscultatory sound signals
16 from being overwhelmed by breathing-related sounds that are substantially
louder than
cardiovascular-based sounds, thereby providing for improving the associated
signal-to-noise
ratio of the cardiovascular-based auscultatory sound signals 16.
Accordingly, in step (710), a next block of Ns contiguous samples of
auscultatory-
sound-sensor time-series data S is acquired over an acquisition period 8 i in
accordance with a
first aspect 800 of a data acquisition process 800, during which time the test-
subject 22 is
instructed to hold their breath. For example, in one set of embodiments, the
nominal acquisition
period 5i is 10 seconds ¨ but at least 5 seconds, -- which, at a sampling
frequency Fs of 24
KHz, results in Ns = 5,*Fs = 240,000 samples.
More particularly, referring again to FIG. 8, the data acquisition process 800
commences with step (802) by pre-filtering the electronic signal from the
associated
auscultatory sound sensor 12, 121', 122', 123 , 121", 122", 123" with the
above-described analog
anti-aliasing filter. Then, from step (804), because breath-held data is to be
acquired, in step
(812), the test-subject 22 is instructed by the operator 48 to hold their
breath. In step (814), if
the operator 48 observes that the test-subject 22 is compliant in holding
their breath, or,
additionally or alternatively, if this is confirmed by a below-described
breath-hold detection
process 1400, then upon manual initiation by the operator 48, in step (816), a
sample counter j
is initialized to a value of one, and, in step (818), the next sample of pre-
filtered auscultatory
sound signal 16 is sampled at the sampling frequency Fs and converted to
digital form by an
associated analog-to-digital (ADC) converter. This process continues until
either Ns = 5i *Fs
samples have been acquired, or the test-subject 22 resumes breathing. More
particularly, in step
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(820), if one or more addition samples remain to be acquired, and if the
operator 48 continues to
observe that the test-subject 22 is holding their breath, or, additionally or
alternatively, if this is
confirmed by a below-described breath-hold detection process 1400, then, in
step (822) the
sample counter./ is incremented, and the next sample is acquired in step
(818). Otherwise, from
step (820), if either all Ns = 5i *Fs samples have been acquired, or if either
the operator 48
observes that the test-subject 22 has resumed breathing, or, additionally or
alternatively, if this
is confirmed by a below-described breath-hold detection process 1400, or, if
the test-subject
22 indicates by their own separate manual switch input that they will resume
breathing, then, in
step (824), the data acquisition process 800 terminates, and the block of
breath-held
auscultatory-sound-sensor time-series data S containing Ns = j samples is
returned to step
(710). In one set of embodiments, if, following step (814), the test-subject
22 is not holding
their breath, the pre-filtered auscultatory sound signals 16 are also
separately-recorded while
waiting for the test-subject 22 to hold their breath, or resume doing so. In
practice, the
auscultatory sound signals 16 typically continue to be recorded between breath-
held segments,
that latter of which are identified by associated recorded start and stop
times with respect to the
associated continuous recording.
Referring also to FIG. 13, alternatively, or additionally, in step (814), the
auscultatory
coronary-artery-disease detection system 10 may further incorporate an
accelerometer 50
operatively coupled to the thorax 20 of the test-subject 22 to provide an
associated acceleration
signal responsive to the motion of the thorax 20. With the test-subject 22
lying on their back at
an inclined angle, for example, at about 30 degrees above horizontal, for
example, as illustrated
in FIG. 4 of U.S. Provisional Application No. 62/568,155 filed on 04 October
2017, entitled
AUSCULTATORY SOUND SENSOR, which has been incorporated by reference herein in
its
entirety, the associated acceleration signal ¨ operatively coupled to
recording module 13 and
possibly transmitted to the docking system 27 --- may be twice integrated
either in recording
module 13 or the docking system 27 to generate a measure of the peak-to-peak
displacement of
the thorax 20, which if greater than a threshold would be indicative of
breathing by the test-
subject 22.
More particularly, referring also to FIG. 14, for an auscultatory coronary-
artery-
disease detection system 10 incorporating an accelerometer 50 operatively
coupled to the
thorax 20 of the test-subject 22, an acceleration signal 52 therefrom may
alternatively or
additionally be processed by an associated breath-hold detection process 1400
to provide for
automatically determining ¨ for example, in step (814) of the data acquisition
process 800
illustrated in FIG. 8 ¨ whether or not the test-subject 22 is breathing,
responsive to the
determination, from the acceleration signal 52, of a peak-to-peak displacement
of the thorax 20
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of the test-subject 22. More particularly, beginning with, and in, step
(1402). the respective
previous/initial values of thorax displacement Yo and thorax velocity Vo,
respectively, are each
initialized to values of zero; a sample counter i is initialized to an initial
value, for example,
zero; the respective minimum YMIN and maximum YMAX values of thorax
displacement are
each set equal to the (initial) value of thorax displacement Yo, the values of
the sample
counter imiN and imAx at which the corresponding minimum ''MIN and maximum
YmAx values
of thorax displacement occur are set equal to the initial value of the sample
counter i; and a
BreathingFlag is set to indicate that the test-subject 22 is breathing. Then,
in step (1404), the
current sample of thorax acceleration A is acquired. Then, in step (1406), if
the sample
counter i is equal to the initial value, i.e. i == 0, then, in step (1408),
the previous value of
thorax acceleration Ao (i.e. initially, the initial value thereof) is set
equal to the value of the
current sample of thorax acceleration A. i.e. Ao == A. and then, in step
(1410), the sample
counter i is incremented, after which the breath-hold detection process 1400
repeats with step
(1404).
Otherwise, from step (1406), if the current sample of thorax acceleration A is
not the
first sample, then, in step (1412), the current value of thorax velocity V is
calculated by
integrating the previous Ao and current A measurements of thorax acceleration,
for example,
using a trapezoidal rule, as follows:
¨ (4 A) cit + Vo
(4)
2
wherein dt is the time period between samples, i.e. dt=1/Fs. Then, in step
(1414), the current
value of thorax displacement Y is calculated by integrating the above-
calculated previous Vo
and current V values of thorax velocity, for example, again using a
trapezoidal rule, as
follows:
¨ (V +v) = di'
(5)
2
Then, in step (1416), the respective previous values of thorax acceleration
Ao, thorax
displacement Yo and thorax velocity Vo are respectively set equal to the
corresponding current
values of thorax acceleration A, thorax velocity V and thorax displacement Y,
respectively,
that will each be used in subsequent iterations of steps (1412) and (1414).
Then, in step (1418), if the current value of thorax displacement Y is greater
than then
current maximum value of thorax displacement YmAx - for example, as would
result during a
phase of chest expansion by the test-subject 22, -- then, in step (1420), the
current maximum
value of thorax displacement YmAx is set equal to the current value of thorax
displacement Y
and the corresponding value of the sample counter 'MAX associated therewith is
set to the
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current value of the sample counter i. Otherwise, from step (1418) ¨ for
example, as would
result from other than a phase of chest expansion by the test-subject 22, --
if, in step (1422), the
amount by which the current value of the sample counter i exceeds the value of
the sample
counter iMAX associated with the maximum value of thorax displacement YMAX is
not equal to
a threshold value 4 (the relevance of which is described more fully
hereinbelow), then in step
(1424), if the current value of thorax displacement Y is less than then
current minimum
value of thorax displacement YMIN ¨ for example, as would result during a
phase of chest
contraction by the test-subject 22, -- then, in step (1426), the current
minimum value of
thorax displacement VIM, is set equal to the current value of thorax
displacement Y and the
corresponding value of the sample counter billy associated therewith is set to
the current value
of the sample counter i. From either steps (1420) or (1426), in step (1428),
if the amount by
which the current maximum value of thorax displacement YmAx is greater the
current
minimum value of thorax displacement YmiN meets or exceeds a displacement
threshold
YmAx, then, in step (1430), the BreathingFlag is set to indicate that the test-
subject 22 is
breathing, after which, in step (1410), the sample counter i is incremented,
after which the
breath-hold detection process 1400 repeats with step (1404). Similarly, from
step (1428), if
the displacement threshold tlYmAx is not exceeded, in step (1410), the sample
counter i is
incremented, after which the breath-hold detection process 1400 repeats with
step (1404).
Further similarly, from step (1424) ¨ for example, as would result from other
than a phase of
chest contraction by the test-subject 22, -- if, in step (1432), the amount by
which the current
value of the sample counter i exceeds the value of the sample counter ininv
associated with the
minimum value of thorax displacement Y1-illy is not equal to the threshold
value 4 in step
(1410), the sample counter i is incremented, after which the breath-hold
detection process
1400 repeats with step (1404).
If, from step (1432), the amount by which the current value of the sample
counter i
exceeds the value of the sample counter iMIN associated with the minimum value
of thorax
displacement Yiwisi is equal to the threshold value A -- following a minimum
chest contraction
of the test-subject 22, in anticipation of subsequent chest expansion, wherein
the threshold
value 4 is greater than or equal to one, -- then, in step (1434), the peak-to-
peak thorax
displacement AY is calculated as the difference between the current maximum
YmAx and
minimum YMIN values of thorax displacement, and, in step (1436), the maximum
value of
thorax displacement YmAx is set equal to the current value of thorax
displacement Y, and the
value of the sample counter imAx at which the corresponding maximum value of
thorax
displacement Yyvix occurred is set equal to the current value of the sample
counter i, in
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anticipation of subsequently increasing magnitudes of the current value of
thorax
displacement Yto be tracked in steps (1418) and (1420).
Similarly, if, from step (1422), the amount by which the current value of the
sample
counter i exceeds the value of the sample counter iMAX associated with the
maximum value of
thorax displacement YmAx is equal to the threshold value LI -- following a
maximum chest
expansion of the test-subject 22, in anticipation of subsequent chest
contraction, wherein the
threshold value d is greater than or equal to one, -- then, in step (1438),
the peak-to-peak
thorax displacement LIY is calculated as the difference between the current
maximum MAXY
and minimum YmiN values of thorax displacement, and, in step (1440), the
minimum value
of thorax displacement YmiN is set equal to the current value of thorax
displacement Y, and
the value of the sample counter imiN at which the corresponding minimum value
of thorax
displacement YmiN occurred is set equal to the current value of the sample
counter i, in
anticipation of subsequently decreasing magnitudes of the current value of
thorax
displacement Yto be tracked in steps (1424) and (1426).
Accordingly, the threshold value LI. provides for a delay to assure that a
most-recent
extremum of displacement has been reached, either the current maximum YMAX or
minimum
YIMIN values of thorax displacement, before calculating the associated peak-to-
peak thorax
displacement LIY.
From either steps (1436) or (1440), in step (1442), if the amount of the peak-
to-peak
thorax displacement LIY calculated in steps (1434) or (1438), respectively,
meets or exceeds
the displacement threshold AYmAx, then, in step (1444), the BreathingFlag is
set to indicate
that the test-subject 22 is breathing. Otherwise, from step (1442), if the
amount of the peak-to-
peak thorax displacement LIY calculated in steps (1434) or (1438),
respectively, does not
exceed the displacement threshold AYA,Lix, then, in step (1446), the
BreathingFlag is reset to
indicate that the test-subject 22 is not breathing. Following either step
(1444) or (1446), in step
(1410), the sample counter i is incremented, after which the breath-hold
detection process
1400 repeats with step (1404).
Referring again to FIG. 7, in step (712), a corresponding block of scaled
auscultatory-
sound-sensor time-series data 'S is calculated by multiplying the acquired
block of
auscultatory-sound-sensor time-series data S by the scale factor SF, and in
step (714), the
scaled auscultatory-sound-sensor time-series data g is analyzed by an
associated debond
detection process 1500 to determine whether or not any of the auscultatory
sound sensors 12,
121', 122., 12', 121", 122", 123" was debonded from skin 38 of the thorax 20
of the test-subject
22 during the associated data acquisition process SOO
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More particularly, referring to FIG. 15, the debond detection process 1500
commences
with step (1502) by initializing a sample counter) to a value of one, and
initializing a threshold
counter TC to a value of zero, wherein the threshold counter TC is a count of
the number of
contiguous successive samples for which the value of the scaled auscultatory-
sound-sensor
time-series data g is less than an associated predetermined debond-detection
threshold DT.
For example, in one set of embodiments, the debond-detection threshold DT is
set to a value
that is about 20% of the achievable maximum value of the output from the anal
og-to-digi tal
converter (ADC). Then, in step (1504), if the absolute value of the sample of
scaled
auscultatory-sound-sensor time-series data S. i.e. IS' (j)I, is less than the
predetermined
debond-detection threshold DT (or, alternatively, if ISF*S(j)1 < DT, thereby
precluding a need
to separately calculate and store scaled auscultatory-sound-sensor time-series
data g), then in
step (1506), the threshold counter TC is incremented, after which, in step
(1508), if the value
of the threshold counter TC does not exceed the number of samples in ND
successive data
segments 46, i.e. TC < ND*5D*Fs, and in step (1510), if the sample counter j
does not exceed
the number of samples Ns in the block of scaled auscultatory-sound-sensor time-
series data g,
then, in step (1512), the sample counter) is incremented, and the process
continues again with
step (1504). Otherwise, from step (1504), if the absolute value of the current
sample of scaled
auscultatory-sound-sensor time-series data g, i.e. I'S (j)1, is not less than
the predetermined
debond-detection threshold DT - indicating that the auscultatory sound sensor
12, 12v, 122',
12', 12", 122", 123" is not debonded from the skin 38 of the thorax 20 of the
test-subject 22, -
- then, in step (1514), the threshold counter TC is reset to a value of zero
and the process
continues with step (1510). Otherwise, from step (1510), if the sample counter
j exceeds the
number of samples Ns in the block of scaled auscultatory-sound-sensor time-
series data S or
auscultatory-sound-sensor time-series data S -- indicating that the entire
block of scaled
auscultatory-sound-sensor time-series data g or auscultatory-sound-sensor time-
series data
S has been screened, -- then the debond detection process 1500 is terminated
with step (1516)
by returning an indication to step (714) of the auscultatory-sound-sensing
process 700 that the
associated auscultatory sound sensor 12, 121', 122', 12", 121", 122", 123" is
not debonded from
the skin 38 of the thorax 20 of the test-subject 22. Otherwise, from step
(1508), if the value of
the threshold counter TC exceeds the number of samples in ND successive data
segments 46,
then the debond detection process 1500 is terminated with step (1518) by
returning an
indication to step (714) of the auscultatory-sound-sensing process 700 that
the associated
auscultatory sound sensor 12, 121', 122', 12', 121", 122", 123" is debonded
from the skin 38 of
the thorax 20 of the test-subject 22. For example, in one set of embodiments,
the value of ND is
equal to 4, and the value of c5D is equal to 1 second.
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Returning to FIG. 7, in step (716), if, from step (714), the debond detection
process
1500 detected that the associated auscultatory sound sensor 12, 121', 122',
12', 121", 122", 123"
was debonded while acquiring the block of auscultatory-sound-sensor time-
series data S. then
the Data Recording Application (DRA) 14 is aborted in step (718), and the
operator 48 is
alerted that one or more auscultatory sound sensors 12, 121', 122', 12', 121",
122", 123" are
debonded, so that this can be remedied. Otherwise, if, from step (714), the
debond detection
process 1500 did not detect that the associated auscultatory sound sensor 12,
121', 122', 12',
121", 122", 123" was debonded while acquiring the block of auscultatory-sound-
sensor time-
series data S. then, in step (720), if sufficient noise-screened data has not
been gathered - for
to example, in one set of embodiments, a total duration of at least 65
seconds of recorded data, --
then the auscultatory-sound-sensing process 700 continues with step (708).
In step (724), an associated noise detection (i.e. noise-screening) process -
operating on
either the block of scaled auscultatory-sound-sensor time-series data S' , or
the block of
auscultatory-sensor time-series data S, in parallel with the debond detection
process 1500 -
provides for detecting if the block of auscultatory-sound-sensor time-series
data S has been
corrupted by excessive noise, and if so, from step (726), that block of
auscultatory-sound-
sensor time-series data S is ignored, and the auscultatory-sound-sensing
process 700
continues by repeating step (710) to acquire a new block of auscultatory-sound-
sensor time-
series data S. Otherwise, from step (726), if the block auscultatory-sound-
sensor time-series
data S has not been corrupted by excessive noise, the process continues with
the above-
described step (720).
From step (720), if sufficient noise-screened data has been gathered for which
the
associated one or more auscultatory sound sensors 12, 121', 122', 123' , 121",
122", 123" were
not debonded from the skin 38 of the thorax 20 of the test-subject 22 - for
example, in one set
of embodiments, a total duration of at least 65 seconds of recorded data, --
then, in step (722), at
least the composite set of blocks of breath-held auscultatory-sound-sensor
time-series data S
acquired in step (710) are subsequently analyzed by an associated Data
Analysis Application
(DAA) 54 operative on the docking system 27 - as illustrated in FIGS. 2 and 3 -
- so as to
provide for detecting an abnormal cardiovascular condition of the test-subject
22. In addition to
recording the segments of breath-held data, alternatively, all data may be
recorded and provided
to the Data Analysis Application (DAA) 54, along with an associated index that
provides for
identifying the corresponding associated breath-held portions thereof for
which the associated
auscultatory sound sensors 12, 121', 122', 123' , 121", 122", 123" were
neither detached nor
debonded from the skin 38 of the thorax 20 of the test-subject 22, nor
corrupted by noise.
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FIGS. 9 and 10a-10f illustrate a simulation of six blocks of breath-held
auscultatory-
sound-sensor time-series data S recorded in accordance with the first aspect
700 of
auscultatory-sound-sensing process 700, with respective durations of 61, 82,
63, Oa, 8s, and So
during which time periods the test-subject 22 was holding their breath,
separated by periods
Ai, 112, 43, A4, and As of normal breathing, wherein FIGS. 10a-10e illustrate
breath-held
auscultatory sound signals 16.1, 16.1' from a normally-bonded auscultatory
sound sensor 12,
121', 122', 12', 121", 122", 123" as illustrated in FIG. 5a, and FIG. 10f
illustrates a breath-held
auscultatory sound signal 16.1, 16.1" from a debonded auscultatory sound
sensor 12, 121',
122', 12', 121", 122", 123", for example, as illustrated in any of FIGS. 5d-
5g, for example, as
might be caused by excessive hair between the adhesive interface 42 and the
auscultatory
sound sensor 12, poor placement of the auscultatory sound sensor 12 on the
thorax 20 of the
test-subject 22, poor angular orientation of the auscultatory sound sensor 12
relative to the
surface of the skin 38, or wrinkled adhesive interface 42 between the
auscultatory sound
sensor 12 and the skin 38, For purposes of simplicity of illustration, FIGS.
10b-10e are
identical to FIG. 10a. However, it should be understood that typically the
amplitude of the
auscultatory sound signals 16, 16.1 varies from heartbeat to heartbeat, and
from one breath-
held segment to another.
Alternatively, one or more of the auscultatory-sound-sensing process 700, the
data
acquisition process 800, the scale-factor-determination process 1300, or the
de-bond
detection process 1500 could be implemented with corresponding alternative
processes
disclosed in U.S. Application Serial No. 16/136,015 filed on 19 September 2018
- with
particular reference to FIGS. 16 through 22 - which is incorporated by
reference herein in its
entirety.
Referring to FIG. 16, in one set of embodiments, all the data is recorded
throughout the
duration of the test, including segments both with and without breathing, and
a set of index
pointers are used to identify locations of associated events, for example,
index pointer arrays
id] and ii[] to store the sample locations at the beginning and end of breath-
held data segments
of the corresponding sampled auscultatory sound data Smil from the Mth
auscultatory sound
sensor 12, and later-used index pointer arrays isil] and is21] to store the
sample locations of the
Si sound at the beginning of each heart cycle, and the S2 sound at the
beginning of diastole
respectively, wherein in FIG. 16, the symbol "B" is used to indicate a period
of breathing, the
symbol "H" is used to indicate a period of breath-holding, and the symbol "D"
is used to
indicate a period of diastole. A status array Status [m, k] indicates the
measurement status of
the le" breath-held data segment of the mth auscultatory sound signal 16, i.e.
the sampled
auscultatory sound data S'n[] from the mu auscultatory sound sensor 12.
Accordingly, step
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(728) -- that provides for ignoring data -- may be implemented by setting the
corresponding
value of the status array Status(m, k) to a value of IGNORE.
Referring to FIGS. 17-20, a noise detection process 1700 called from step
(724)
provides for determining whether or not a particular segment of breath-held
auscultatory
sound signal 16.1 is corrupted by excessive noise, and if so, provides for
flagging that segment
as being excessively noisy so as to be prospectively excluded from subsequent
processing.
Generally the noise detection process 1700 generates, in step (1714),
frequency-domain noise
filters FH[] responsive to cross-correlations of frequency spectra of pairs of
adjacent
auscultatory sound sensors 12. Accordingly, sensor-index pointers ml[p] and m2
[p] provide
to for identifying the associated auscultatory sound sensors 12, ml[p],
m2[p] of each pair, the
latter of which is identified by a pair pointer p. For each pair of adjacent
auscultatory sound
sensors 12 in a set of adjacent pairs -- selected by sensor-index pointers
ml[p] and m2[p] in
steps (1706) and (1710), respectively, wherein the pair pointer p is
initialized to 1 in step
(1704) -- the noise detection process 1700 generates, in step (1714), a
frequency-domain noise
filter FH[] by cross-correlating, in step (1802), the frequency spectra FSAI]
and FSBI] --
generated in steps (1708) and (1712) -- of each associated breath-held
auscultatory sound
signal 16.1: SAH and SBIL wherein the values of the frequency-domain noise
filter FH[] are
generated by normalizing the frequency spectra of the cross-correlation
function to a range of 0
to 1 in step (1804), then subtracting these values from unity, and then
setting resulting values
that are less than a noise floor to the value of the noise floor in step
(1806). Accordingly, when
used to multiply the frequency spectra FSA11 and FSB11 in step (1904) as
called from steps
(1718) and (1720) for frequency spectra FSA11 and FSB[1, respectively, the
frequency-domain
noise filter F111] provides for attenuating the components of the breath-held
auscultatory
sound signal 16.1: SA[] and SBI] that are correlated with one another.
Accordingly, the
operation of step (1904) provides for a matched filtering process to
accentuate the underlying
noise in the associated breath-held auscultatory sound signal 16.1: SA[] or
SB[] by
attenuating frequency-component portions thereof that are correlated with a
corresponding
breath-held auscultatory sound signal 16.1: SB[] or SA[] from the other
auscultatory sound
sensor 12, ml1p1, m2[p] of the pair. Furthermore, in step (1904), the product
of the frequency-
domain noise filter FH[] with either of the frequency spectra FSAI] or FSBI]
is inverse Fourier
transformed back to the corresponding time domain noise signal SN[]. Then, in
steps (1908)
through (1918), a plurality of NFF-r-point short-time Fourier transform (STFT)
arrays are
generated, for example, with NFFT=1024, with each overlapped with respect on
one another by a
half width, i.e. NFFr /2, after which the associated average spectral power
array FX11 in dB is
calculated in step (1920). Then, the average powers PLOW, FMB), PHIGH in three
respective
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frequency ranges 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz to 1,500 Hz,
respectively, is
calculated in steps (1922), (1924) and (1926), respectively, wherein each
average power PLOW,
PMID, PHIGH is compared with a corresponding threshold -- for example, in one
set of
embodiments, -20 dB, -50 dB and -30 dB, respectively ¨ in step (1928). The
corresponding
breath-held auscultatory sound signal 16.1: SA[] or SB[] is then flagged in
step (1930) as
being noisy if any of the associated noise power thresholds are exceeded.
Referring to FIG. 20,
if one auscultatory sound sensor 12 exceeds the noise threshold in a given kth
breath-held
segment of breath-held sampled auscultatory sound data Sm[io[k]: ii[k]], that
auscultatory
sound sensor 12, m is flagged in step (2008) as being noisy so as to later be
ignored for that lith
breath-held segment. If a particular auscultatory sound sensor 12, in exceeds
the noise
threshold for more than one breath-held segment of breath-held sampled
auscultatory sound
data Sm[io[k]: ii[k]], then that auscultatory sound sensor 12, m is flagged in
step (2016) as
being bad. For each breath-held segment k of data, the process of steps (1706)
through (1722)
repeats for each of NPAIRS pairs of adjacent auscultatory sound sensors. For
example, referring
to FIG. 3, in one set of embodiments, there are three pairs of adjacent
auscultatory sound
sensors, i.e. NPAIRS=3, as follows: for p=1, the auscultatory sound sensors 12
at the left and
sternum second intercostal spaces L2, S2; for p=2, the auscultatory sound
sensors 12 at the
left and sternum third intercostal spaces L3, S3; and for p=3, the
auscultatory sound sensors
at the left and sternum fourth intercostal spaces L4, S4. The process of steps
(1704) through
(1726) is repeated until all the breath-held segments k of data have been
processed.
More particularly, referring to FIG. 17, the noise detection process 1700
commences
with step (1702) by initializing a breath-held-segment pointer k to a value of
1, so as to
provide for pointing to the first breath-held segment of data. Generally, the
breath-held-
segment pointer k provides for pointing to the kth breath-held segment of
data, of duration
Si, extending between sample locations iolkl and NM as illustrated in FIG. 16.
Then, in step
(1704), the pair pointer p is initialized to a value of 1, and a noise counter
NNOISY is initialed to
a value of 0. Then, in step (1706), the k' breath-held segment of breath-held
sampled
auscultatory sound data Smi[Pl[io[k]: ii 110 is selected as the sampled
auscultatory sound data
SAL] of the first auscultatory sound sensor 12, ml [p] of the pair p, and in
step (1708) the
Fourier Transform of the sampled auscultatory sound data SAL] is calculated as
FSAI] =
FFT(SAH). Similar, then, in step (1710), the kth breath-held segment of breath-
held sampled
auscultatory sound data Sm21PI[io[k]: ii 110 is selected as the sampled
auscultatory sound data
SBH of the second auscultatory sound sensor 12, m2[p] of the pair p, and in
step (1712) the
Fourier Transform of the sampled auscultatory sound data SAH is calculated as
FSBH =
FFT(SBH).
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Then, in step (1714), the frequency-domain noise filter FH[] generated by a
matched-
noise-filter generation process 1800 that -- referring also to FIG. 18 --
commences in step
(1802) with the cross correlation of the frequency spectra FSAI], FSB1] of the
sampled
auscultatory sound data SAL], SBI] of the first fillip] and second m2[p]
auscultatory sound
sensors 12 of the pair p, wherein the resulting cross-correlation is stored in
array FH[] and then
normalized to a range of 0 to / in step (1804), and then inverted in step
(1806), wherein each
element of the normalized array FM] is subtracted from 1, and if the result is
less than a noise
floor, is set to the value of the noise floor NoiseFloor. Then, in step
(1808), the resulting
frequency-domain noise filter FH[] is returned and subsequently used in steps
(1716) and
(1718) of the noise detection process 1700 to evaluate the noise in the
frequency spectra
FSA1], FSBI] of the sampled auscultatory sound data SAL], Sig] of the first
ml[p] and second
m2[p] auscultatory sound sensors 12 of the pair p, respectively, in accordance
with an
associated noise-content-evaluation process 1900, the latter of which is
called from step (1716)
to evaluate the noise content of the frequency spectrum FSAI] of the sampled
auscultatory
sound data SAO of the first auscultatory sound sensor 12, in lip] of the pair
p, and which is
called from step (1718) to evaluate the noise content of the frequency
spectrum FSB[] of the
sampled auscultatory sound data SBI] of the second auscultatory sound sensor
12, m2[p] of
the pair p.
Referring to FIG. 19, the noise-content-evaluation process 1900 commences in
step
(1902) with receipt of the index m of the associated auscultatory sound sensor
12, in, the
associated frequency spectrum FS[] of the associated auscultatory sound sensor
12, m, and
the associated frequency-domain noise filter FH[]. Then, in step (1904), the
time domain
noise signal SN I] ¨ containing a total of NPTS data points, i.e. the number
of data points in the
breath-held sampled auscultatory sound data Sm[io[k]: [k]] -- is given by the
inverse Fourier
Transform of the product of the associated frequency spectrum FS[] with the
associated
frequency-domain noise filter FH[], corresponding to a time-domain cross-
correlation of the
corresponding associated time-domain signals. Then, in step (1906), each of
the NFFT
summation data points of a frequency-domain summation array FXSUM[] is
initialized to
zero, as is an associated summation counter Nsum, wherein the number of
summation data
points NFFT is a power of 2, for example, 1024, and substantially less than
the total number of
data points NPTS in the time domain noise signal SN[]. Then, in step (1908),
an index jmix --
to the first sample of an Nm-point window of samples to be analyzed from the
time domain
noise signal SN[] ¨ is initialized to a value of 1. Then, in step (1910), an
index NAX -- to the
last sample of the Nm-point window of samples to be analyzed from the time
domain noise
signal SNI] ¨ is set to the value of jivnN + NFFT ¨ 1. Then, in step (1912),
if the end of the time
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domain noise signal SN[] has not been reached, then, in step (1914), the
square of values ¨ i.e.
corresponding to noise power -- of an NFFT-point Fourier Transform of the data
from the NFFT-
point window of samples of the time domain noise signal SN[] over the range of
samples
SNUAHN] to SN[jmAx], is added to the frequency-domain summation array FXSUM1].
Then,
in step (1916), the summation counter Nsum is incremented, and in step (1918),
the index jAIIN
is incremented by half the width of the NFFT-point window, i.e. by a value of
NFFT /2, so as the
provide for the next NFFT-point window to be analyzed to overlap the current
NFFT-point
window by half the window width. The above noise-content-evaluation process
1900 repeats
beginning with step (1910), until, in step (1912), the index imAx exceeds the
end of the time
domain noise signal SN[], after which, in step (1920), each of the values of
the frequency-
domain summation array FXSUMI] is divided by the value of the summation
counter Nsum
so as to calculate an associated average noise power FX[]. Then, in steps
(1922), (1924) and
(1926), respectively, the noise power is summed in three different
corresponding respective
frequency ranges, for example, 20 Hz to 200 Hz, 200 Hz to 800 Hz, and 800 Hz
to 1,500 Hz,
respectively, to give three corresponding respective power levels, PLOW, PMID
and PHIGH,
respectively. Then, in step (1928), if any of the power levels, PLOW, Pmm or
PMGH exceeds a
corresponding respective power threshold value ThresholdLow, Thresholdmiu or
ThresholdinGn, then, in step (1930), the noise threshold is considered to have
been exceed for
the particular auscultatory sound sensor 12, m and the particular associated
segment k of
breath-held sampled auscultatory sound data Sm[io[k]: [k]] is flagged with a
corresponding
indication of an associated status in an associated NoiseStatus flag.
Otherwise, from step
(1928), if none of the power levels, PLOW, PMID or PMGH exceeds the
corresponding respective
power threshold value Thresh IdLow, Thresh 'dim]) or Thresh Idnicu, then, in
step (1932),
the noise threshold is considered to have not been exceed for the particular
auscultatory sound
sensor 12, m and the particular associated segment of breath-held sampled
auscultatory
sound data Sm[io[k]: ii[k]] is flagged with a corresponding indication of the
associated status in
the NoiseStatus flag. The results from either steps (1930) or (1932) are then
logged by an
associated results-logging process 2000, which is called from step (1934).
Referring to FIG. 20, the results-logging process 2000 commences with receipt
in step
(2002) of the index m of the associated auscultatory sound sensor 12, m and
the associated
NoiseStatus flag. If, in step (2004), the NoiseStatus flag indicates a noisy
auscultatory sound
sensor 12, m, then, in step (2006), the noise counter N ArOISY is incremented,
and, in step (2008),
the corresponding element of the status array Status[m, k] for the associated
auscultatory
sound sensor 12, m and breath-held segment k is updated to activate an
associate Noisy flag,
so as to indicate the associated auscultatory sound sensor 12, m being noisy
for the associated
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breath-held segment k. Then, in step (2010), if the value of the noise counter
NNOISY is greater
than 1, then, in step (2012), the corresponding elements of the status array
Status [m, k] for
each of the auscultatory sound sensors 12, m is updated to activate the Ignore
flag, so as to
provide for ignoring each of the auscultatory sound sensors 12, m for the
associated breath-
held segment k. Then, or otherwise from step (2010), in step (2014), if the
Noisy flag of the
status array Status[m, k] is activated for at least NFAIL breath-held segments
k for the
associated auscultatory sound sensor 12, m, then, in step (2016), status array
Status [m] For
the associated auscultatory sound sensor 12, m is updated to activate the Bad
flag for the
associated auscultatory sound sensor 12, m, so as to indicate that the
associated auscultatory
sound sensor 12, m is bad. Then, or otherwise from either step (2004) or step
(2014), the
results-logging process 2000 terminates by returning to the noise detection
process 1700.
More particularly, referring again to FIG. 17, in step (1720), if all of the
pairs of adjacent
auscultatory sound sensors 12 have not been processed, then, in step (1722),
the pair pointer
p is incremented, and the noise detection process 1700 is repeated for the
next pair of
auscultatory sound sensors 12, ml[p], m2[p], beginning with step (1706) for
the same breath-
held segment k. Otherwise, from step (1720), if in step (1724), additional
segments of breath-
held sampled auscultatory sound data Sm[io[k]: ii[k]] remain to be processed,
then, in step
(1726), the breath-held-segment pointer k is incremented, and the noise
detection process
1700 is repeated beginning with step (1704) for the next segment of breath-
held sampled
auscultatory sound data Sm[io[k]: [MI. Otherwise, from step (1724), the noise
detection
process 1700 terminates with step (1728).
Referring again to FIG. 1, in accordance with one set of embodiments, the
results from
the docking system 27 may be transferred to a server computer system 56, for
example, for
subsequent transfer to an external data storage or processing system 58, for
example, to
provide for either viewing or analyzing the results at a remote location.
Referring again to
FIGS. 2 and 4, in one set of embodiments, the composite set of blocks of
breath-held
auscultatory-sound-sensor time-series data S are screened prior to analysis by
the associated
Data Analysis Application (DAA) 54, for example, by first segmenting the set
of blocks of
breath-held auscultatory-sound-sensor time-series data S by heart cycle with
an associated
segmentation process 60, and then validating the associated heart cycle data
with an associated
heart cycle validation process 62, for example, to provide for additional
screening for heart-
phase associated noise.
Referring to FIG. 21, an auscultatory sound signal preprocessing and screening
process 2100 provides for preprocessing breath-held auscultatory sound signals
16.1 and an
associated electrographic signal 37 from an ECG sensor 34, 34', for example,
as acquired, for
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example, in accordance with the above-described auscultatory-sound-sensing
process 700,
with concurrent recording of an associated electrographic signal 37 from an
ECG sensor 34,
34'. In accordance with one embodiment as illustrated, in step (2102) a good
beat counter GB
is initialized to zero, and a segment counter ksEG is initialized to a value
of 1, wherein the good
beat counter GB provides a count of the number of heart cycles for which the
associated
breath-held sampled auscultatory sound data S[] is not corrupted by either
outliers or noise
during diastole, and the segment counter ksEG is the value of the breath-held-
segment pointer
k for the current block B of breath-held sampled auscultatory sound data S[].
Then, from step (2104), referring to FIG. 22a, a segment of breath-held
auscultatory
sound signals 16.1 is acquired and preprocessed by an associated auscultatory
sound signal
acquisition and filtering process 2200, wherein, in step (2202), for each
auscultatory sound
sensor 12, in step (2204), a block of data of associated breath-held
auscultatory sound signals
16.1 is acquired, for example, in accordance with the auscultatory-sound-
sensing process 700,
or as an associated segment k of a previously acquired and recorded breath-
held sampled
auscultatory sound data Sm[io[k]: ii[11 for the mth auscultatory sound sensor
12. With data
recorded at a sampling frequency Fs of 24 kHz, and for portions of the
spectrum of the breath-
held auscultatory sound signals 16.1 of interest related to coronary artery
disease limited to a
few hundred Hertz ¨ i.e. without significant acoustic energy above 500 Hz --
the sampling rate
may be reduced to 2 kHz. Accordingly, in step (2206), the breath-held
auscultatory sound
signal 16.1 is filtered by a fourth order Type II Chebyshev low-pass filter
having a cut-off
frequency of 500 Hz to avoid aliasing, and then, in step (2208), decimated to
a 2 kHz sampling
rate using a phase invariant method that also involves low-pass filtering, so
as to generate
corresponding filtered-decimated breath-held sampled auscultatory sound signal
64.
The breath-held auscultatory sound signal 16.1 in some cases can include very
low
frequency ¨ for example, around 10 Hz -- vibrations that are believed to be
associated with
movements of the entire auscultatory sound sensor 12 stimulated by chest
motion. Considering
the sensor housing as an inertial mass under a tangential component of
gravitational force
attached to elastic surface, it is possible to initiate sensor vibration by
small surface
displacements. Such vibrations can be amplified by resonance characteristics
of the tissue-sensor
interface. Depending on the Q-factor of tissue-sensor system, vibrations may
decay very slowly,
extending well into diastolic interval of heart beat, contaminating the signal
of interest with
relatively large amplitude unwanted interference. The net effect of such
interference is an
unstable signal baseline and distortion of the actual underlying heart sounds.
Potential sources of
noise relevant to digitized acquisition of acoustic signals include: electric
circuit thermal noise,
quantization noise from the AID converter, electro-magnetic interference,
power line 60 Hz
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interference and acoustic noises relevant to human physiology and the
environment where
recording is done (ambient noise). Generally thermal noise power and A/D
converter
quantization noise are very low for the bandwidth of interest and may be
significant only for the
signals with amplitude in microvolt region. Furthermore, recording artifacts
may significantly
reduce visibility of the signal of interest since these artifacts may have
relatively high amplitude
and may overlap in spectral content. These artifacts are due to uncontrolled
patient movements,
signals related to respiration, and sensor vibrations produced by the
associated oscillating mass
of the auscultatory sound sensor 12 coupled to the elastic skin tissue surface
(cardio
seismographic waves). The latter type of artifact may be caused by the
inertial mass of the sensor
to housing and can be relatively high in amplitude due to resonance
properties of the sensor-tissue
interface. Although the frequency of such vibrations is relatively low (around
10 Hz), the
associated relatively high amplitude thereof can result in an unstable signal
baseline, which
complicates the detection of target signals.
However cardiac activity may also produce low frequency signals ¨ for example,
as a
result of contraction of heart muscle, or as a result of valvular sounds --
that may have valuable
diagnostic information. In some situations, the spectrum of the artifacts may
overlap with the
acoustic spectrum of cardiac signals such as myocardium vibrations. Therefore,
it can be
beneficial to reduce baseline instability so as to provide for recording
primarily acoustic signals
originating from the cardiac cycle.
In accordance with one set of embodiments, these very low frequency artifacts
may be
rejected by using additional signal filtering to suppress the associated
characteristic vibration
frequencies, for example, using a software-implement high-pass filter 66
having a 3 dB cut-off
frequency above 10 Hz, for example, as provided for by a Savitzky-Golay-based
high-pass
filter 66', wherein, in step (2210), the filtered-decimated breath-held
sampled auscultatory
sound signal 64 is smoothed by a Savitzky-Golay (SG) smoothing filter 68 to
generate a
smoothed breath-held sampled auscultatory sound signal 70, the latter of
which, in step
(2212), is subtracted from the filtered-decimated breath-held sampled
auscultatory sound
signal 64 to then generate the corresponding resulting high-pass-filtered
breath-held sampled
auscultatory sound signal 72 having a relatively-higher signal-to-noise ratio
(SNR), but without
significant distortion of the original filtered-decimated breath-held sampled
auscultatory
sound signal 64.
The digital Savitzky-Golay smoothing filter 68 is useful for stabilizing
baseline
wandering (for example, as may be exhibited in ECG signals) and provides for
removing the
low-frequency signal components without causing significant ringing artifacts.
The Savitzky-
Golay smoothing filter 68 employs a least squares approximation of a windowed
signal using a
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polynomial function. The associated parameters of the Savitzky-Golay smoothing
filter 68
include the window size M in samples that defines the associated cut-off
frequency, and the
polynomial order N used for the approximation. The associated roll-off range
is fairly wide and
the cut-off frequency is somewhat arbitrary. For example, for the Savitzky-
Golay smoothing
filter 68 used in step (2210), the associated window size M ¨ expressed in
terms of window
time duration tw ¨ is in the range of 8 milliseconds to 100 milliseconds, with
N=3. For
example, a window time duration tw = 8 milliseconds provides a cut-off
frequency of
approximately 100 Hz, and a window time duration tw = 25 milliseconds,
provides for passing
signal frequencies above 40 Hz through the Savitzky-Golay-based high-pass
filter 66'.
The Savitzky-Golay smoothing filter 68 is defined by a least-squares fit of
windowed
original samples, with an Nth degree polynomial,
AT
y(n) =laknk
(6)
k=0
so as to minimize the associated error function, E:
M N
E = aknk ¨ x[n]
(7)
\ k=0
wherein the total window width is 2M+1 samples. The associated short-time
window sliding
through entire time series fits the data with a smooth curve. The frequency
response of the
Savitzky-Golay smoothing filter 68 depends strongly on the window size M and
polynomial
order N. The normalized effective cut-off frequency of the Savitzky-Golay
smoothing filter
68 is empirically given as follows, wherein f = 0),/7, for which (psis the
radian sampling
frequency:
N +1
f = (8)
3.2M ¨ 4.6
Following the high-pass filter 66, 66' of steps (2210) and (2212), from step
(2214), the
auscultatory sound signal acquisition and filtering process 2200 is repeated
beginning with
step (2202) for each of the auscultatory sound sensors 12, after which, from
step (2216), the
resulting blocks of high-pass-filtered breath-held sampled auscultatory sound
signals 72 --
for each of the auscultatory sound sensors 12-- are returned to step (2104).
Then, also from step (2104), referring to FIG. 22b, a corresponding segment of
a
concurrent electrographic signal 37 is acquired and preprocessed by an
associated
electrographic signal acquisition and filtering process 2200', wherein, in
step (2204'), a
block of electrographic data 74 of the corresponding electrographic signal 37
from the ECG
sensor 34, 34' ¨ in correspondence with the blocks of high-pass-filtered
breath-held sampled
auscultatory sound signals 72 returned by the above-described auscultatory
sound signal
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acquisition and filtering process 2200 ¨ is acquired, for example, in
accordance with the
above-described data acquisition process 800, or as an associated segment k of
previously
acquired and recorded electrographic data 74. Then, in step (2206'), and as
explained more
fully herein below, the electrographic data 74 is filtered by a fourth order
Type II Chebyshev
low-pass filter having a cut-off frequency of 40 Hz, and then, in step
(2208'), is decimated by a
factor of ten, so as to generate corresponding filtered-decimated
electrographic signal 76,
which, in step (2216'), is returned to step (2104).
Referring again to FIG. 21, in step (2106), the high-pass-filtered breath-held
sampled
auscultatory sound signals 72, or alternatively, the breath-held auscultatory
sound signals
to 16.1, are segmented to identify the starting and ending points of each
heart cycle, and to identify
the starting and ending points of each associated diastole region or phase,
thereof.
In accordance with a first aspect, this may be accomplished using the breath-
held
auscultatory sound signals 16.1 -- or the corresponding associated high-pass-
filtered breath-
held sampled auscultatory sound signals 72 ¨ alone, without relying upon the
associated
electrographic signal 37 from the ECG sensor 34, 34', or upon the
corresponding associated
filtered-decimated electrographic signal 76, for example, in accordance with
the following
portions of the disclosure and drawings of U.S. Patent No. 9,364,184:
Abstract, FIGS. 1-50,
Col. 1, Line 1 through Col. 3, line 59 (indicated), Col. 5, line 1 through
Col. 34, line 55
(indicated), and the claims, which are incorporated herein by reference.
However, in accordance with a second aspect, the electrographic signal 37 from
the
ECG sensor 34, 34', and particularly, the corresponding associated filtered-
decimated
electrographic signal 76 responsive thereto, provides an effective basis for
segmenting the
breath-held auscultatory sound signals 16.1, 72 by heart cycle, after which
the high-pass-
filtered breath-held sampled auscultatory sound signal 72 may then be used to
locate the
associated dominant Si and S2 heart sounds that provide for locating the
associated heart phases
of systole and diastole, data from the latter of which provides for detecting
coronary artery
disease responsive to information in the breath-held auscultatory sound
signals 16.1, 72.
Referring also to FIG. 23, in accordance with the second aspect, a heart-cycle
segmentation and heart-phase identification process 2300 is called from step
(2106) of the
auscultatory sound signal preprocessing and screening process 2100 to locate
the heart
cycles responsive to the filtered-decimated electrographic signal 76, to then
segment the high-
pass-filtered breath-held sampled auscultatory sound signal 72 by heart cycle
responsive
thereto, and to then identify the region of diastole within each heart cycle
(and implicitly, to
therefor also identify the remaining region of systole) from an analysis of
the high-pass-filtered
breath-held sampled auscultatory sound signal 72 alone.
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The normal human cardiac cycle consists of four major intervals associated
with different
phases of heart dynamics that generate associated audible sounds: 1) the first
sound (Si) is
produced by closing of mitral and tricuspid valves at the beginning of heart
contraction, 2)
during the following systolic interval, the heart contracts and pushes blood
from ventricle to the
rest of the body, 3) the second sound (S2) is produced by the closing of
aortic and pulmonary
valves and 4) during the following diastolic interval, the heart is relaxed
and the ventricles are
filled with oxygenated blood.
Referring to FIGS. 24 and 25, respectively illustrating a breath-held
auscultatory
sound signal 16.1, 72 and a corresponding electrographic signal 37, 76
associated with six
heartbeats, each QRS complex 78 illustrated in FIG. 25 is generated by an
associated
ventricular depolarization that precedes cardiac contraction. The Si heart
sound resulting from
the closing of mitral and tricuspid valves is observed immediately after R-
peak , and the S2
heart sound resulting from the closing of aortic and pulmonary valves is
observed at the end of
T-wave of the ECG cycle. This relative timing of the heart sounds Si, S2 and
the QRS
complex 78 of the associated electrographic signal 37, 76 provides for using
the
electrographic signal 37 to locate the beginning and end of each heart cycle
without relying
upon the breath-held auscultatory sound signal 16.1, 72 to do so. For each
heartbeat, the Si
heart sound -- marking the start of systole ¨ follows shortly after the R-
peak, and the S2 heart
sound ¨ marking the end of systole and the beginning of diastole ¨ follows
thereafter, with
diastole continuing until the next R-peak.
Although the QRS complex 78 is the most prominent feature, the electrographic
signal
37, 76 may be distorted by low frequency baseline wandering, motion artefacts
and power line
interference. To stabilize the baseline, a Savitzky-Golay-based high-pass
filter 66' ¨ similar to
that used in steps (2210/2212) when filtering the breath-held auscultatory
sound signal 16.1 --
may be used to cancel low-frequency drift, for example, prior to the
subsequent low-pass
filtering, in step (2206'), by the fourth order Type II Chebyshev low-pass
filter, for example,
having a 40 Hz cut-off frequency, and/or prior to decimation of the sampling
by factor of 10 in
step (2208'), which together provide for both reducing high frequency noise
and emphasizing
QRS complexes 78.
More particularly, referring again to FIG. 23, and also referring to FIG. 26,
the heart-
cycle segmentation and heart-phase identification process 2300 commences in
step (2302) by
calling an electrographic segmentation process 2600 that provides for
identifying the locations
of the R-peaks. More particularly, in step (2602), a block of electrographic
data x[] is
received, and, in step (2604), normalized so as to have a maximum absolute
value of unity.
Then, referring also to FIGS. 27 and 28, from step (2606), beginning with step
(2702) of an
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associated first envelope generation process 2700, the block of filtered-
normalized
electrographic data x[] is used to generate an associated electrographic
envelope waveform
80, Fs[] that emphasizes the R-peaks of the filtered-normalized electrographic
data x[],
wherein, for each value of index k -- set in step (2702) -- the corresponding
value of the
electrographic envelope waveform 80, Fs[k] is set in step (2704) responsive to
a sum of values
within a sliding window containing a subset of Nw points, as follows:
1 N'Y
Fs (t(0) = --(x(k + i))4 ln(x(k+i))4
(9)
Equation (9) is similar to a Shannon energy function, but with the associated
discrete
signal values raised to the fourth power ¨ rather than a second power -- to
emphasize difference
between R-peaks 80' and baseline noise. The value of the electrographic
envelope waveform
80, Fs[] is calculated for each of the Npm values of index k until, in step
(2706), all points have
been calculated, after which, in step (2708), the electrographic envelope
waveform 80, Fs[] is
returned to step (2606) of the electrographic segmentation process 2600.
Returning to FIG. 26, in step (2608), in accordance with a first aspect, the
peaks of the
electrographic envelope waveform 80, Fs[] are located, for example, by finding
indices k for
which the associated value of the electrographic envelope waveform 80, Fs[k]
is a local
maximum exceeding a fixed, predetermined threshold. For example, FIG. 28
illustrates the
electrographic envelope waveform 80, Fs[] and associated R-peaks 80' thereof,
for a portion
of a 10-second recording of a filtered-decimated electrographic signal 76.
The electrographic segmentation process 2600 for finding R-peaks is quite
simple and
stable when signal-to-noise ratio (SNR) of recording is sufficiently high.
Otherwise, when the
electrographic data 74 is excessively noisy, additional signal processing such
as discrete
wavelet decomposition may be used to further enhance R-peaks and facilitate
QRS detection.
Occasional relatively high amplitude transients due to patient movements or
ambient interference
may produce false peaks unrelated to QRS complex, and therefore complicate
segmentation.
Referring to FIG. 29, in step (2610), the R-peaks 80' of the electrographic
envelope
waveform 80, Fs[] are then validated by a peak validation process 2900, both
with respect to
temporal location ¨ Le. relative spacing -- and magnitude ¨ i.e. deviation
from a median value, --
so as to verify that the detected R-peaks 80' correspond to R-peaks 80' rather
than noise. More
particularly, in step (2902), for each of the detected R-peaks 80', in step
(2904), if the
difference in temporal locations of each pair of R-peaks 80' is less than a
time threshold TMIN,
or if, in step (2906), the difference between the magnitude P(tprAx(i)) of
each R-peak 80'and
the median value of the magnitudes of all R-peaks 80' is greater than a
magnitude threshold
VmAx, then, in step (2908), a status indicator associated with the R-peak 80'
being tested is set to
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IGNORE, in order to prevent the associated R-peak 80' from being used for
subsequent
segmentation of the breath-held auscultatory sound signal 16.1, 72. Otherwise,
from step
(2910), the peak validation process 2900 terminates in step (2912) and returns
to step (2612) of
the electrographic segmentation process 2600, which, in turn, returns the peak
locations
tPEAKI] to step (2302) of the heart-cycle segmentation and heart-phase
identification process
2300, wherein valid R-peaks SO' satisfy both of the following conditions:
It PEAK (1 +1) t PE4K (1)1 I Ad' W
(10)
and
(t PE (0)¨ median (13)1ViviAx
(11)
1() wherein tpEAK(i) is the time of the ith R-peak SO' and P(tpEAK(i)) is
the corresponding magnitude
of the R-peak 80'.
Referring again to FIG. 26, alternatively, in accordance with a second aspect,
the R-
peaks 80' of the electrographic envelope waveform 80, Fs[] are located in step
(2608) by an
associated R-peak detection process 5400 that provides for discriminating
valid R-peaks 80'
from noise or other anomalies. Referring to FIGS. 54 and 55, the R-peak
detection process
5400 determines the peak values and associated samples times of the
electrographic envelope
waveform SO, Fs[] within a plurality of sample windows 81, each of window
width tw, and
each offset from one another with an associated hop period tuop. More
particularly, the R-peak
detection process 5400 commences with step (5402) by setting a window start
time tmEsT to
point to the beginning of the electrographic envelope waveform 80, Fs[1, and
by initializing a
window counter Nw to a value of /. Then, in step (5404), the maximum value of
the
electrographic envelope waveform 80, Fs[] is determined within the sample
window 81
extending between sample times tynN and twin' + tw, and stored in element
Pw[Nw] of window-
peak array Pw[1, wherein, for example, the window width tw is sufficiently
wide to span a
single heart cycle, for example, with tw equal to about 1.5 seconds. Then, in
step (5406), the
corresponding sample time at which the associated peak value occurred is
stored as a
corresponding element tpEAx_w[Nw] of an associated window-peak-time array
tpEAx_vd].
Then, in step (5408), the window start time tmiN is incremented by the hop
size tHop, and, in
step (5410), if the prospective end Nix + tw of the prospective next sample
window 81 is
before the end tEND of the electrographic envelope waveform 80, Fs[], then, in
step (5412), if
the most-recently detected R-peak 80' is unique, then, in step (5414), the
window counter Nw
is incremented. Then, or otherwise directly from step (5412), the R-peak
detection process
5400 repeats beginning with step (5404). For example, in one set of
embodiments, the hop size
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tHop is set to half the shortest heart cycle, but no less than 0.3 second, or
a corresponding
fraction of the window width tw, although it should be understood that the
particular values of
the window width tw and hop size tHop are not limiting. Although depending
upon the window
width tw and hop size tHop, different sample windows 81 may potentially span
the same R-
peak 80', step (5412) provides for ensuring that each of the R-peaks 80' in
the window-peak
array Pw[] are unique, and that the associated sample times in the window-peak
array Pw[] are
in monotonically increasing order.
Then, from step (5410) following R-peak detection within the last sample
window 81, in
step (5416), a minimum peak-threshold PmiNum is set equal to about 60 percent
of the median
amplitude of the R-peaks 80' within the window-peak array Pw[], and, in step
(5418), a
maximum peak-threshold PMAXLIM is set equal to twice the median amplitude of
the R-peaks
80' within the window-peak array Pw[]. Then, in steps (5420) through (5430), R-
peaks 80'
that are outside those limits are ignored, so as to provide for ignoring noise
or other spurious
signal components. More particularly, beginning in step (5420), a second
window counter iw -
- that provides pointing to the above-detected unique R-peaks 80' -- is
initialized to a value of I,
and a peak counter NPEAK -- that provides for counting the number of R-peak
80' within the
above-determined amplitude thresholds -- is initialized to a value of 0. Then,
in step (5422), if
the value of the second window counter iw is less than the number of windows
Nw determined
above in steps (5402) through (5414), then, in step (5424), if the magnitude
of the currently-
pointed-to R-peaks SO', i.e. Pw[iw], is greater than the minimum peak-
threshold Pmpirtm and
less than the maximum peak-threshold PMAXLIM - indicating a potentially valid
R-peak 80' ¨
then, in step (5226), the peak counter NPEAK is incremented, and the
corresponding magnitude
and time of the associated R-peak 80', i.e. Pw[iw] and tPEAK WRW1, are stored
as respective
values in in a corresponding peak array PINpEAKI and peak-time array
tPEAKINPEAK1
(alternatively, the window-peak array Pw[] and the window-peak-time array
tpEAK w 1] could
be reused in place for storing these values). For example, referring again to
FIG. 55, step (5424)
provides for ignoring occurrences of noise and a noise or spurious signal --
the magnitudes of
each of which are less than the minimum peak-threshold PMINLIM, -- and
provides for ignoring
the example of a false R-peak 80' that resulted from electro-static discharge
(ESD), and which
exceeded the maximum peak-threshold PMAXLIM Then, in step (5430), the second
window
counter iw is incremented to point to the next entry of the window-peak-time
array tpEAK wil,
and the R-peak detection process 5400 repeats with step (5422). If, from step
(5422), all
entries of the have been processed, then, in step (5432), the process returns
control to step
(2608) of the electrographic segmentation process 2600.
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Referring again to FIG. 26, following step (2608) and completion of the
associated
second-aspect R-peak detection process 5400, a second aspect of a peak
validation process
5600 provides for validating the R-peaks 80' detected by the second-aspect R-
peak detection
process 5400. More particularly, referring also to FIG. 56, in steps (5602)
through (5608), the
associated heart-cycle period DT[iprAx] between each pair of temporally-
adjacent R-peaks 80'
is calculated in step (5604), indexed by a second peak counter SPEAK that is
initialized to a value
of I in step (5602) and incremented in step (5608) so long as, in step (5606),
the value thereof is
less then NPEAK-1 that had been determined in steps (5424) and (5426) of the
second-aspect R-
peak detection process 5400. Then, in step (5610), a minimum heart-cycle-
period-threshold
DTA/nNum is set to 0.3 seconds (corresponding to a heart rate of 200 BPM).
Then, in step
(5612), a corresponding heart-rate HRH array is calculated from the heart-
cycle period DT[]
array, wherein each element of the former is given by the inverse of the
corresponding element
of the latter, after which the median heart-rate HRmEDEAN is determined a the
median of the
values of the elements of the heart-rate array HRH. Then, in step (5618) a
status STATUS[]
array to initially indicate that each R-peak 80' associated with the heart-
rate array HR[] is
"OK", and the second peak counter SPEAK that is initialized to a value of 1.
Then, if, in step
(5618), the heart-cycle period DTPPEAK1 is not greater than the minimum heart-
cycle-period-
threshold DTmiNum, or, in following step (5620), the heart-rate HR[] is not
both greater than
60 percent of the median heart-rate HRMEDIAN AND less than 140 percent of the
median
heart-rate IIRmEDIAN, then, in step (5622) the STATUS[ipEAK] of the
corresponding R-peak
80' is set to "IGNORE". Then, or otherwise from steps (5618) and (5620), if,
in step (5624),
all R-peaks 80' have not been processed, then, in step (5626), the second peak
counter SPEAK is
incremented, and the second-aspect peak validation process 5600 repeats with
step (5618).
Otherwise, from step (5628), the process returns control to step (2610) of the
electrographic
segmentation process 2600.
Referring again to FIG. 23, in step (2304), for each auscultatory sound sensor
12, in
step (2306), the associated high-pass-filtered breath-held sampled
auscultatory sound signal
72 thereof is segmented based upon the locations of the associated R-peaks 80'
in the
corresponding filtered-decimated electrographic signal 76. For example, FIG.
30 illustrates a
correspondence between the breath-held auscultatory sound signal 16.1, 72 and
the
corresponding electrographic envelope waveform 80, Fs[], wherein the locations
of the R-
peaks 80' in the latter are used to segment the former by heart cycle.
Furthermore. FIG. 31
illustrates a stack of segments of the high-pass-filtered breath-held sampled
auscultatory
sound signal 72, each segment corresponding to a different corresponding heart
cycle 82, 82.1,
82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9 -- each resulting from a single
corresponding heart
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beat -- within the corresponding continuous high-pass-filtered breath-held
sampled
auscultatory sound signal 72 illustrated in FIG. 30.
The exact location of the first Si and second S2 heart sounds produced by the
respective closing of atrioventricular and semilunar valves is somewhat
ambiguous, and can be
particularly difficult to locate in situations when the associated breath-held
auscultatory sound
signals 16.1 are recorded in an environment with relatively high level ambient
noise, or if there
are associated heart murmurs resulting from turbulent blood flow. However,
even under
relatively-poor recording conditions, the first Si and second S2 heart sounds
of the cardiac
cycle remain the most prominent acoustic features.
to Referring again to FIG. 23, following segmentation of the high-pass-
filtered breath-
held sampled auscultatory sound signal 72 into corresponding resulting
associated heart-cycle
segments in step (2306) -- each comprising one heart cycle 82, resulting from
a single
corresponding heart beat, ¨ beginning with step (2308), for each of the heart
cycles 82 located
in step (2306), in step (2310) a Shannon energy envelope is generated from the
corresponding
associated portion of the high-pass-filtered breath-held sampled auscultatory
sound signal
72 in accordance with a corresponding second envelope generation process 3200,
which is
similar to the above-described first envelope generation process 2700 except
for the associated
envelope function. More particularly, referring to FIG. 32, beginning with
step (3202) of the
second envelope generation process 3200, for the kth of NSAMPLES samples of a
block of high-
pass-filtered breath-held sampled auscultatory sound data 72, sl] of the
corresponding
associated heart cycles 82, a corresponding value of an associated acoustic
envelope waveform
84, Es [k] is generated in step (3204), responsive to a sum of values within a
short-time sliding
window ¨ with different windows overlapping one another -- containing a subset
of Nw points,
as follows in accordance with a canonical Shannon energy function Es[k] that
provides a
measure of the energy of the underlying high-pass-filtered breath-held sampled
auscultatory
sound signal 72, s[]:
1 'vw
(t(k))=--(S(k +0)2 in(S(k i))2
(12)
Nyv 1=1
The value of the acoustic envelope waveform 84, Es [k] is calculated for each
of the NSAMPLES
values of index k until, in step (3206), all points have been calculated,
after which, in step
(3208), the acoustic envelope waveform 84, Es[k] is returned to step (2310) of
the heart-cycle
segmentation and heart-phase identification process 2300.
For envelope generation by each of the first 2700 and second 3200 envelope
generation
processes illustrated in FIGS. 27 and 32, the associated signal x[], sl], from
which the
corresponding envelope Fs11, Esil is generated, is zero-padded at the
beginning and at the end
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with (Nw ¨ 1)/2 zeros, assuming Nw is an odd integer, wherein the first data
point in the padded
signal x[], s[] would then begin at index 1+(Nw ¨ 1)/2. After that, sliding
window with size Nw
and stride = 1 is used to compute windowed values of the associated envelope
function, so as to
provide for generating a time-series of the same length as the associated
signal x[], s[], without a
time shift relative thereto.
Referring again to FIG. 23, in step (2312), the acoustic envelope waveform 84,
Es[k]
generated in step (2310) is smoothed to reduce local fluctuations therein, for
example using
either a moving average filter, or a Savitzky-Golay smoothing filter 68, for
example, with an
associated window size of tw = 8.3 milliseconds.
to FIG. 33 illustrates an example of an acoustic envelope waveform 84,
Es[] in
correspondence with a rectified version 86 (i.e. containing an absolute value)
of the associated
high-pass-filtered breath-held sampled auscultatory sound data 72, s[] from
which the
acoustic envelope waveform 84, Es[k] was generated, wherein the respective
envelope peaks
88si,
88S2 associated with the corresponding S/ and S2 heart sounds can be readily
identified in
the acoustic envelope waveform 84, Es1].
Referring again to FIG. 23, in step (2314), the locations of the envelope
peaks 88", 88s2
associated with the corresponding Si and S2 heart sounds are identified using
an adaptive
threshold method that iteratively adjusts an associated threshold value down
from a maximum
value until the two most prominent envelope peaks 88", 88s2 emerge within the
associated
heart cycle 82, above a particular threshold limit. Referring also to FIG. 34,
the final position of
envelope peaks 88", 88s2 associated with the corresponding Si and S2 heart
sounds is found
as the apex of a local quadratic model 90.1, 90.2 of the acoustic envelope
waveform 84, Es I]
as described more fully hereinbelow. Adaptive thresholding provides for
accommodating
substantial variation in the relative magnitude of the envelope peaks 88",
88s2 that might occur
either from one heart beat to another, from one patient to another, or from
one recording site to
another.
Then, in step (2316), the locations of the envelope peaks 88s1, 8.,6S2
associated with the
corresponding Si and S2 heart sounds are validated using a normalized acoustic
envelope
waveform 84, Es[], i.e. normalized to a range of 0 and 1, and the associated
local quadratic
models 90.1, 90.2 thereof, in accordance with a minimum-time-spacing criteria
used to remove
or ignore spurious transient peaks unrelated to heart sounds, similar to
equation (10) above that
is associated with step (2904) of the above-described peak validation process
2900 used to
validate the electrographic envelope waveform 80, Fs[].
Then, in step (2318), the acoustic envelope waveform 84, Esrl is searched
relative to
the associated indices list_pEAK, kS2_PEAK -- respectively associated with the
corresponding
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respective envelope peaks 88s1, 88s2 -- to find adjacent data points therein, -
- i.e. having
associated indices kst-, kst-F, ks2-, ks2+ -- for which the corresponding
values of the acoustic
envelope waveform 84, Es(ksi), Es(lisi+), Es(ks2-), Es(ks2+) are each about 5
percent down,
i.e. 95 percent of, the corresponding values ES(ES1_PEAK), ES(kS2_DEAK) of the
associated
envelope peaks 88s1, 88S2.
Then, in step (2320), respective local quadratic models ES1(k), 90.1' and
ES2(k),
90.2' are fitted ¨ for example, by least-squares approximation -- to the three
points associated
with each of the corresponding respective envelope peaks 88", 88s2 as follows:
ES1(k) =Quadratic Fit ( {ksi-, Es (ksi-)) , {kSl_PEAK, ES(kSl_PEAK)} , {kS1+,
ES(kS1+)} ) (13a)
ES2(k) =Quadratic Fit ( {ks2-, ES (kS2-)} {kS2_PEAK, ES(kS2_PEAK)} {kS2+,
ES(kS2+)} ) (13b)
Then, referring again to FIG. 34, in step (2322), the respective pairs of
roots (ksi_srART,
ksi_END) and (ksz_srART, ksz_END) of the local quadratic models ES1(k), 90.1'
and ES2(k),
90.2' are solved, such that ES1(ks1_srART) = ES1(ks1_END) = 0 and ES2(k.
=
ES2(kS2_END) = 0, wherein ksi_srART < kSl_PEAK< kSLEND and lisz_sTART <
kS2_PEAK < kS2_END=
is Referring to FIGS. 70 and 71, in addition to the Si and S2 sounds, S3,
S4 or S5 sounds
may be present in the heart cycles 82 for some test-subjects 22. The purpose
of identifying Si,
S2, S3, S4, and S5 regions is to help extract or derive useful features (such
as Indicators that
correlate highly with the existence of coronary artery disease (CAD)) from
heart sounds. In turn,
these features can be used in direct detection and machine learning for
discriminating CAD
patients from non-CAD patients. The prerequisite of many feature extractions
is to correctly
identify the S/ and S2 sounds.
The S3, S4, and S5 sounds do not appear for every patient. But each may
indicate one or
more cardiac conditions, such as, CAD, Aortic Insufficiency, Aortic Stenosis,
Luminal
Irregularities, and Mitral Regurgitation. The regions within diastole during
which the S3 and
S5 sounds may occur are located relative to the diastasis region of diastole,
which is a relative
rest period of the heart during mid-to-late diastole, during which period the
heart motion is
minimal. The region during which the S4 sound may occur is located relative to
the R-peak 80'
at the end of the heart cycle 82 and the beginning of the next heart cycle 82.
The starting point of the diastasis region is determined using what is
referred to as the
Weissler or Stuber formula for the period of delay DT -- in milliseconds --
from an R-peak 80'
to the starting point of the diastasis region, given by the following:
DT,s = (AT
R¨R ¨350)=0.3 350
(14)
wherein ATR-R is the time interval in milliseconds between R-peaks 80'. In one
set of
embodiments, this is approximately the ending point for the region most likely
to include S3, i.e.
the S3 region. Accordingly, the starting point for the S3 region is determined
by advancing
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relative to the starting point of diastasis - or, equivalently, the end point
of the S3 region -- by a
time interval A 1,53. For example, in this set of embodiments, the time
interval commences at
about 100 milliseconds prior to the starting point of diastasis and ends at
the starting point of
diastasis. In another set of embodiments, the time interval of the S3 region
is taken to extend
from about 60 milliseconds prior, to 60 milliseconds after, the starting point
of diastasis. The S3
swing is calculated by subdividing the S3 region of the associated breath-held
auscultatory
sound signal 16.1, for example, band-pass-filtered breath-held sampled
auscultatory sound
data s[] having a pass-band in the range of 20, 25, or 30 Hz to 40 or 50 Hzõ
into a series of- i.e.
one or more -- time intervals, and calculating or determining one or more of
the difference
to between the maximum and minimum amplitude values -- i.e. maximum
amplitude - minimum
amplitude, -- the minimum amplitude, and the maximum amplitude, for each
interval in the S3
region.
In addition to the above time-domain analysis, the associated breath-held
auscultatory
sound signal 16.1 is also analyzed in the frequency domain using a Short-Time
Fourier
Transform (STFT), for example, in one set of embodiments, having a 1 Hz
frequency resolution
and a 0.0025 second time resolution -- but generally, using frequency and time
resolutions that
may be empirically adjusted to improve detection and discrimination - in
cooperation with an
associate windowing method, for example, using Chebyshev windowing on a
sliding window
that is moved along the S3 region of the breath-held auscultatory sound signal
16.1. The
frequency-domain features for each heart beat are generated by calculating the
mean and median
of each of the windows of the STFT.
The S3 swing values and frequency-domain features are saved for further
calculations
and/or for use as an input to one or more of the below-described
classification processes. In
view of there being relatively few patients that clearly exhibit an S3 sound,
an unsupervised
clustering method is applied on all the generated features to classify heart
beats into two clusters
that respectively include "with S3- and "without S3- heart beats. S3 is
analyzed on a beat-by-
beat basis. Given the S3 is not an intermittent signal, one strategy is to
analyze all heart beats
from a given patient, and if S3 appears in more than one third of all the
heart beats, that patient
would be identified as having S3. There are some patients who exhibit a low-
ejection fraction
ratio (e.g. an ejection fraction (E. F.) lower than 35%) that are highly
likely to have S3.
However some of these patients have CAD, and some have other types of cardiac
arrhythmia.
Once the unsupervised clustering is applied followed by voting among all beats
belonging to one
patient, if those patients are found in the cluster "with S3", this would
provide for validating that
the clustering matches reality.
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The S4 region is located in a final portion of diastole, starting at the P
wave on the ECG
signal. However, in anticipation of a prospective absence of the P wave in
some ECG
recordings, an approximate starting point for the S4 region may be used, for
example, a time
interval of ATS4, for example, from 10 to 20 percent of the period of the
heart cycle 82, for
example, about 100 to 200 milliseconds in a 1 second duration heart cycle 82,
in advance of the
R-peak 80' at the end of the heart cycle 82, or equivalently, in advance of
the beginning of the
S/ sound of the next heart cycle 82. The S4 region of the associated breath-
held auscultatory
sound signal 16.1, e.g. the band-pass-filtered breath-held sampled
auscultatory sound data
s[] having a pass-band in the range of 20, 25, or 30 Hz to 40 or 50 Hz, is
subdivided into a
series of intervals and the associated S4 swing within each interval is
calculated as the absolute
value of the difference between maximum and minimum amplitude magnitudes of
the raw or
high-pass-filtered data within that interval in the S4 region. The S4 swing is
calculated separately
for audible (above 20 Hz) and inaudible (below 20 Hz) frequency ranges, for
which the
configurations of the associated signal filters are specific to the particular
frequency range.
Generally, the minimum and maximum values of the signal will depend upon the
associated
frequency range of the associated signal, and the type of filter used to
generate the filtered signal.
The S2 swing is also similarly calculated over the associated S2 region, in
addition to
calculating the 54 swing as described hereinabove. The ratio of the S4 swing
to S2 swing
provides a measure of the likelihood that a patient exhibits an S4 sound, with
that likelihood
increasing with increasing value of the S4-to-S2 swing ratio. For example,
FIG. 71 illustrates a
heartbeat for which the S4-to-S2 swing ratio has a value of 0.49 for the
illustrated channel 1 of
the band-pass-filtered breath-held sampled auscultatory sound data s[].
The S2 and S4 swing values, and/or the S4-to-S2 swing ratio, are saved for
further
calculations and/or for use as an input to one or more of the below-described
classification
processes. In accordance with one approach, the mean value of the S4-to-S2
swing ratio is
calculated for an entire population of patients, beat by beat, and then the
median of S4-to-S2
swing ratio is taken across all beats of each patient. For those patients for
which the S4-to-S2
swing ratio is greater than the associated mean value of the S4-to-S2 swing
ratio are identified as
having an S4 sound for purposes of training the associated below-described
classifier, from
which an associated threshold value is determined that ¨ in combination with
other factors --
provides for discriminating patients with CAD from patients without CAD, after
which the
threshold value of the S4-to-S2 swing ratio is then applied to the associated
test set.
The SS region is identified as the end of the S3 region to the start of the S4
region.
Accordingly, the starting point is determined using the above-described
Weissler or Stuber
formula. As mentioned, this is approximately the ending point for the S3
region. The ending
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point of the S5 region is located at the beginning of a time interval of A
Ts4, for example, about
100 milliseconds, in advance of the R-peak 80' at the end of the heart cycle
82, or equivalently,
in advance of the beginning of the Si sound of the next heart cycle 82. The SS
swing is
calculated by subdividing the SS region of the associated breath-held
auscultatory sound
signal 16.1, e.g. the band-pass-filtered breath-held sampled auscultatory
sound data s[]
having a pass-band in the range of 20, 25, or 30 Hz to 40 or 50 Hz, into a
series of intervals and
calculating the absolute value of maximum amplitude ¨ minimum amplitude for
each interval in
the SS region. The SS swing values may be saved for further calculations
and/or for use as an
input to one or more of the below-described classification processes.
to If, in step (2324), all heart cycles 82 in the high-pass-filtered
breath-held sampled
auscultatory sound signal 72 have not been processed, then the heart-cycle
segmentation and
heart-phase identification process 2300 is repeated beginning with step (2308)
for the next
heart cycle 82. Otherwise, from step (2326), if all auscultatory sound sensors
12 have not
been processed, then the heart-cycle segmentation and heart-phase
identification process
2300 is repeated beginning with step (2304) for the next auscultatory sound
sensor 12.
Otherwise, from step (2326), in step (2328), the heart-cycle segmentation and
heart-phase
identification process 2300 returns either the mean values k51, ks2 of the
corresponding root
locations IliSl_START, kSLENDI, IkS2_START, ks2_ENDI associated with the
corresponding Si and
S2 heart sounds, or the corresponding mean values tsi, t52 of the associated
times, i.e.
{t(lisi_START), t(ksi_END)), {t(1{,S2_START), t(k52_Erin)}, for each of the
heart cycles 82 in each of
the high-pass-filtered breath-held sampled auscultatory sound signals 72 from
each of the
associated auscultatory sound sensors 12.
Referring to FIG. 35, segmented high-pass-filtered breath-held sampled
auscultatory
sound data 72, s[] from each of the heart cycles 82, 82.1, 82.2, 82.3, 82.4,
82.5, 82.6, 82.7,
82.8, 82.9 may be synchronized with respect to the mean time ts2 associated
with the S2 heart
sound of each heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8,
82.9, as illustrated in
FIG. 35, or alternatively, may be synchronized with respect to the mean time
tsi associated with
the Si heart sound of each heart cycle 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6,
82.7, 82.8, 82.9,
wherein the region of diastole extends from the time associated with the
second root ks2_END , i.e.
t(ks2_END), associated with the S2 heart sound, to the end of the associated
heart cycle 82, 82.1,
82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9.
Referring again to FIG. 21, either the breath-held auscultatory sound signal
16.1, or
the corresponding high-pass-filtered breath-held sampled auscultatory sound
signal 72, is
first analyzed within each region of diastole for each heart cycle 82
identified in step
(2106/2300), and for auscultatory sound sensor 12, m to identify any outliers,
and to detect, for
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selected pairs of auscultatory sound sensor 12, excessive noise. More
particularly, beginning
with step (2107), a heart-cycle pointer kBEAT is first initialized to a value
of 1, after which, via
step (2108), for each heart cycle 82 and for each auscultatory sound sensor
12, m, in step
(2110), each region of diastole of either the breath-held auscultatory sound
signal 16.1, or the
corresponding high-pass-filtered breath-held sampled auscultatory sound signal
72, i.e.
extending between samples isilksEG, kBEATJ and isilksEG, kEEATd-ll is checked
for outliers, e.g.
random spikes, using a beat outlier detection process 3600, wherein the heart-
cycle pointer
kBEAT points to the particular heart cycle 82 within the selected breath-held
segment ksEG, and
isilksEG, kEEATJ corresponds to the kS2_END of the associated heart cycle 82
at the beginning of
to diastole, and isilksEG, kBEAT-id corresponds to the end of diastole of
that heart cycle 82, and the
beginning of the systole for the next heart cycle 82. Referring to FIG. 36,
the beat outlier
detection process 3600 commences in step (3602) with receipt of the identifier
m of the
auscultatory sound sensor 12, the associated sampled auscultatory sound data
Sm[], the
segment counter ksEG, and the heart-cycle pointer kBEAT. Then, in steps (3604)
and (3606), a
standard-deviation counter listninv is initialized to a value of 1, and an
index jmiN is set equal
to isziksEG, kBEAT], the location of the beginning of diastole in the sampled
auscultatory sound
data Sm[], respectively. Then, steps (3608) through (3616) provides
for repetitively
calculating, in step (3612), a standard deviation STD[lisTmicv] of the sample
values Sm[jmix :
jmAxl in a plurality of windows of NWINDOW samples, each window shifted by one
sample with
respect to the next in steps (3614) and (3616) -- that increment ksTrinEv and
jmIN, respectively, -
- resulting in a standard deviation array STD11 containing ksmnEv standard
deviation values,
wherein, in step (3608), jmAx = jmiN + NWINDOW -1. For example, in one set of
embodiments,
NWINDOW is equal to 128. Then, in step (3618), the standard deviation array
STD[] is rank
ordered, and the median value thereof, MedianSTD, is used in step (3620) to
calculate a
standard deviation compactness metric STDDEVCM, as follows:
Max(DH)¨medianStd
STDDEVCM = ___________________________________
medianStcl ¨Min(DH)
(15)
Then, in step (3622), if the standard deviation compactness metric STDDEVCM
exceeds a
threshold, for example, in one set of embodiments, equal to 6, but generally
between 1 and 10,
the particular region of diastole for the particular breath-held segment from
the particular
auscultatory sound sensor 12, m, is flagged as an outlier in step (3624).
Then, or otherwise
from step (3622), in step (3626), the process returns to step (2110) of the
auscultatory sound
signal preprocessing and screening process 2100.
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Referring again to FIG. 21, then, in step (2112) of the auscultatory sound
signal
screening process 2100, if an outlier was detected in step (3622) and flagged
in step (3624) of
the beat outlier detection process 3600, then in step (2120), if the end of
the breath-held
segment has not been reached, i.e. if kBEAT < NBEATS(kSEG), then the
auscultatory sound signal
preprocessing and screening process 2100 repeats beginning with step (2108)
for the next
heart cycle 82.
Otherwise, from step (2112), referring again to FIGS. 17-20, the above-
described noise
detection process 1700 may be called from step (2114) to provide for
determining whether or
not a particular region of diastole for a particular heart cycle 82 of either
the breath-held
auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-
held sampled
auscultatory sound signal 72, is corrupted by excessive noise, and if so,
provides for flagging
that region of diastole as being excessively noisy so as to be prospectively
excluded from
subsequent processing, wherein above references to breath-held auscultatory
sound signal
16.1 in the description of the associated noise detection process 1700 should
be interpreted as
referring to the corresponding region of diastole, i.e. between samples is2
IksEG, ksEivrl and
isiRsEG, kBEAr+1] for the kBEATth heart cycle 82 of segment ksEG of either the
breath-held
auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-
held sampled
auscultatory sound signal 72, and references to the breath-held segment k in
the associated
noise detection process 1700 ¨ which in this case would not be incremented in
step (1726), with
kmAx=k in step (1724), -- should be interpreted as referring to a
corresponding particular heart
cycle 82 associated with the particular region of diastole, wherein the noise
detection process
1700 would terminate with step (1934) of the associated noise-content-
evaluation process
1900.
Then, returning to the step (2116) of the auscultatory sound signal screening
process
2100, if a noise threshold was exceeded in step (1930) of the noise-content-
evaluation process
1900, in step (2120), if the end of the breath-held segment has not been
reached, i.e. if kBEAr <
NBEArs(ksEG), then the process repeats beginning with step (2108) for the next
heart cycle.
Otherwise, from step (2116), the good beat counter GB is incremented in step
(2118) before
continuing with step (2120) and proceeding therefrom as described hereinabove.
Otherwise,
from step (2120) if the end of the breath-held segment has been reached, in
step (2122), if a
threshold number of valid heart cycles has not been recorded, the process
repeats with step
(2104) after incrementing the segment counter ksEG in step (2123). Otherwise,
the recording
process ends with step (2124).
Accordingly, each breath-holding interval B, lisEG of either the breath-held
auscultatory sound signal 16.1, or the corresponding high-pass-filtered breath-
held sampled
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auscultatory sound signal 72 is segmented into individual heart beats 82 (i.e.
heart cycles 82,
wherein reference to heart cycles 82 is also intended as a short-hand
reference to the associated
breath-held sampled auscultatory sound data S[], and each heart beat 82 is
associated with a
corresponding heart cycle 82, the latter of which comprises a plurality of
heart phases associated
with the pumping action of the heart and the actions of the associated heart
valves) and the
diastolic interval D is analyzed to determine the associated noise level to
provide for quality
control of the associated breath-held sampled auscultatory sound data S[], so
as to provide
for signal components thereof associated with coronary artery disease (CAD) to
be detectable
therefrom. Quality control of the recorded signals provides for detecting weak
signals that may
to indicate health problems but can otherwise be blocked by strong noise or
unwanted interference.
The present method is developed for quantitative control of signal quality and
can be deployed in
the recording module 13 for real-time quality monitoring or can be used at the
post recording
stage to extract only low-noise heart beats 82 that satisfy specific quality
condition.
Referring to FIG. 37, a second aspect of an auscultatory sound signal
preprocessing
and screening process 3700 -- also referred to as a beat selection algorithm,
as was the first
aspect auscultatory sound signal preprocessing and screening process 2100 --
provides for
collecting heart beats 82 that have diastolic noise level below the specific
mean noise power
level threshold Po. As described hereinabove, the associated auscultatory-
sound-sensing
process 700 proceeds through sequential acquisition of heart sound intervals
with duration
between 5 and 15 sec (for example, breath-holding intervals 13). In step
(3702), the sampled
auscultatory sound data Sm[], the identifier in of the associated auscultatory
sound sensor 12,
and the associated segment counter ksEG are received, and, in step 3704), the
corresponding
recorded data block is passed through the heart-cycle segmentation and heart-
phase
identification process 2300 that identifies the beginning and the end of each
heart beat 82
using synchronized ECG recording or another signal processing code that
identifies timing of the
first (Si) and second (S2) heart sounds, as described hereinabove. The second-
aspect
auscultatory sound signal preprocessing and screening process 3700 input
parameters also
include the mean noise power level threshold Po and the required number
NU/if/AT of high
quality heart beats 82. In step (3706), a good beat counter GB is initialized
to a value of 0.
Following the heart-cycle segmentation and heart-phase identification process
2300, a two-
dimensional array of heart beats 82 is created, for example, as illustrated in
FIGS. 31 and 35.
The heart-cycle segmentation and heart-phase identification process 2300 also
identifies
timing of diastolic interval D of each heart beat 82. Beginning with the
selection of the first
heart cycle 82 in step (3708), in step (3710), the breath-held sampled
auscultatory sound
data S[] is normalized with respect to absolute value of the S2 peak, and, in
accordance with a
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first aspect, in step (3712), the mean noise power P is computed within a time
window T. The
time-window T, is slid along the diastole D with the 50% overlap to compute an
array of
localized signal power Pr]. After the power P[iw] of the sampled auscultatory
sound data
SI] is calculated within each time-window Tw indexed by index ivv, the maximum
power
PMAX of diastole D is determined i.e. as given by the maximum of the array of
localized signal
power P[], and, in step (3714), is compared against the mean noise power level
threshold Po,
the latter of which in one set of embodiments, for example, has a value of ¨20
dB. A noise
power level P kw] in one of the time-windows Tw greater than the mean noise
power level
threshold Po may indicate either excessive noise or presence of a large
amplitude transient
outlier.
Referring to FIG. 38, in accordance with a second aspect of heart-beat quality
assessment
procedure, the quality test consists of two stages designed to detect high
amplitude transient
spikes and noisy beats with broad-band spectrum. The quality tests are
performed on the
diastolic portion of the heart cycle 82, which is the region of primary
interest for CAD
detection. Nonstationary signals can be analyzed using a relatively short time
sliding time-
window Tw, for example, in one set of embodiments, with I', = 30 ms (Nn, = 120
samples all; =
4 kHz), which is slid across diastole, with each time-window T, advanced in
time with respect
to the previous time-window Tw by a stride period. In one set of embodiments,
the stride period
is equal to the duration of the time-window Tw, so that adjacent time-windows
Tw abut one
another without overlap. The total number of time-windows Tw (i.e. sub-
segments) in diastole,
without overlap, is given by Ns =
where N is the total number of samples during diastolic,
and N. is the number of samples in each time-window T.
The variance within each time-window T ¨ used to detect any outliers therein ¨
is
computed as:
N-"v-
6, 2 = 1 - )2 (16)
k=1
wherein xik and fix are respectively the km sample and the mean value, of the
jth time-window
Tw, respectively. The local signal power of the ith time-window Tw, is given
by:
P, = ,2
(17)
An outlier power threshold Pum is determined by adding 6 dB to the median
value of P1 for all
time-windows T,, and in step (3714), if the value of Pi exceeds PLim for any
time-window Tõ,
then, in step (3720), the current heart cycle 82 is ignored.
If none of the time-windows Tw include an outlier, then the mean power of
diastole is
given by:
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1 Ns
P ¨
(18)
mS -1
The associated noise power threshold PTII is defined with respect to the 2-
byte AID converter
range, so that:
PADC =10'
(19)
wherein Th is a predetermined threshold, for example, -50 dB, and Pc is the
power associated
with the maiximum signed range of a 2-byte AID converter, i.e. (32767)2.
Accordingly, if, in
step (3714), the mean diastole power Pm exceeds the noise power threshold Pm,
then, in step
(3720), the current heart cycle 82 is ignored.
If, from step (3714), the diastolic signal power exceeds the mean noise power
level
threshold Po, then, in step (3720), the associated heart beat 82 is labeled as
noisy beat and is
not counted in the overall tally of heart beats 82. Otherwise, from step
(3714), the good beat
counter GB in step (3716), and if, in step (3718), if the number of good heart
beats 82, i.e. the
value of the good beat counter GB, is less than the required number NG3HAT of
high quality
heart beats 82, then the second-aspect auscultatory sound signal preprocessing
and
screening process 3700 repeats with step (3708) for the next heart cycle 82.
In one set of
embodiments, if the required number of high quality heart beats 82 is not
reached within a
reasonable period of time, then the user is informed that recording is
excessively noisy so that
additional actions can be performed to improve signal quality.
Referring to FIGS. 73-76, as another example, either as an alternative to, or
in addition
to, the above-described second aspect heart-beat quality assessment procedure
implemented in
steps (3712) and (3714) of the second aspect auscultatory sound signal
preprocessing and
screening process 3700, a third aspect of a heart-beat quality assessment
process 7400 is
responsive to the statistical properties of heart beat signals treated as a
random time-series
process within an ensemble of beats acquired during the recording time,
generally in accordance
with the following two steps: 1) segmentation of the recorded breath-held
sampled
auscultatory sound data S[] into individual heart beats 82 as described
hereinabove, and 2)
estimating statistical parameters of each heart beat 82 relative to an
associated ensemble
average, wherein the discrete signal value of a particular heart beat 82, 82.j
at a specific time tn
is considered as a normally distributed random variable xn, and the entire
heart beat 82, j can be
considered as a random vector in the multidimensional space with
dimensionality defined by the
signal duration and the sampling rate. For example, referring to FIG. 73,
using a stack of
segmented heart beats 82, the third-aspect heart-beat quality assessment
process 7400
identifies those heart beats 82, 82.j that significantly deviate from an
associated ensemble
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median with respect to one or more of an associated vector magnitude, an
associated vector
hyper-angle, and a duration of the associated heart beat 82, j. Such
deviations can result from
either significant noise, motion artifacts or acoustic interference signals
irrelevant to heart
function. For example, a stack of segmented heart beats 82, 82.1, 82.2, 82.3,
82.4, 82.5, 82.6,
82.7. 82.8. 82.9. 82.10-- which include noisy heart beats 82 -- is illustrated
in FIG. 73.
More particularly, referring to FIG. 74, the third-aspect heart-beat quality
assessment
process 7400 commences in step (7402) with receipt of the breath-held sampled
auscultatory
sound data Sm[t] for a plurality of heart cycles 82, 82.j, b, followed by
segmentation of the
heart cycles 82, 82.j, b in step (7404), so as to generate a plurality of
signals xj, each of which
is associated with a different heart cycle 82, 82.j, b, with the heart-cycle
period Ti of each
heart cycle 82, 82.j, b determined in step (7406). Each signal xi associated
with each ¨ i.e. the
heart beat 82, 82.j can be considered as a random vector xj =
xj,N1 ,
with the total number of samples N of each random vector xj given by the
number of samples of
the longest duration heart beat 82, 82.j sampled at a sampling rate fs over
the duration of the
longest beat period T, the latter of which is determined in step (7408) as the
maximum of the
heart-cycle periods Ti of the associated Nb heart cycles 82, 82.j, b, i.e. j=1
to Nbõ with N then
being determined in step (7410) as the product of the longest beat period T
and the sampling
rate fs, i.e. as given by N = T*fs. Then, in step (7412), each of the
resulting Nh random vectors
xi
is associated with a corresponding heart beat 82, 82.1, 82.2, 82.3,
82.4, 82.5, 82.6, 82.7. 82.8. 82.9. 82.10, and the set of random vectors
xi =
x I are synchronized with respect to one another at time zero
corresponding to the start of each heart cycles 82, 82.j, b, as illustrated in
FIG. 73, wherein the
collection of samples Y
,X2,n,== = X10,n1 from each of the heart beats 82, 82.1, 82.2, 82.3,
82.4, 82.5, 82.6, 82.7. 82.8. 82.9. 82.10 associated with a particular sample
n at a particular
sample time tn relative to the start of each of the associated heart beats 82,
82.1, 82.2, 82.3,
82.4, 82.5, 82.6, 82.7. 82.8. 82.9. 82.10 are considered to be normally
distributed, as illustrated
by the normal distribution 7300 in FIG. 73. Then, in step (7414), signals xj
associated with
heart beats 82, 82.j that are shorter in duration than the longest beat period
T in the stack are
sufficiently padded with zeros 7302 so as to provide for a total of N samples
in each
signal/random vector xj.
As a vector object, each signal Xj can be characterized by an associated
difference-
vector magnitude dj and an associated vector hyper-angle aj (or, for example,
the cosine
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thereof) with respect to a reference direction. Accordingly, in step (7416),
the median signal Xm
associated with a median heart beat 82' of the ensemble of segmented heart
beats 82, 82.1,
82.2, 82.3, 82.4, 82.5, 82.6, 82.7. 82.8. 82.9. 82.10 is determined by
computing the median value
of each corresponding random variable xj,n, for n = 1, N of the ensemble,
wherein the value of
each sample xm(n) of the median vector xm associated with a corresponding
median heart beat
82' is given by xm(n)=Median(xj,n for j=1 to Nb). Following the determination
the median heart
beat 82', then, as a counterpart to steps (3712) through (3720) of the
auscultatory sound
signal preprocessing and screening process 3700, supra, in step (7418), a
heart-cycle
pruning process 7500 provides for assessing the quality of each heart beat 82,
82.j responsive
1() to one or more of 1) a difference-vector magnitude di, 2) a vector
hyper-angle oti (or the
cosine thereof), and 3) a deviation of the heart-cycle period Ti, and for
pruning relatively-lower-
quality heart cycles 82, 82.j, b, if any, responsive thereto.
More particularly, referring to FIG. 75, the heart-cycle pruning process 7500
commences, in step (7502), for each of the plurality of heart cycles 82, 82.j,
b associated with
the heart beats 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7. 82.8. 82.9.
82.10 associated with the
median heart beat 82' determined in step (7416), supra. Then, in step (7504),
a scaler
difference-vector magnitude di is determined from the vector distance between
the random
vector xj associated with the particular heart cycles 82, 82.j, b, and the
corresponding
associated median vector xm, as follows:
r _v
2 '\
d =11xi = ((xi ), (Xm ),) (20)
1=1
Then, in step (7506), if the difference-vector magnitude di does not exceed a
corresponding
difference-vector magnitude threshold, then, or if step (7504) had been
bypassed, in step (7508),
a scaler vector hyper-angle ad (or the cosine thereof) associated with the
selected heart beat 82,
j is calculated as follows:
(
DO = (Xm),
X *X
a . = cos' j Al - ,=1
- cos' (21)
\IXJ11.11XMII N 1 1
(x1)2 , = L(x.),
2
wherein xi -X,, is the dot product of vectors -xi and XM. Then, in step
(7510), if the vector
hyper-angle cti (or the cosine thereof) does not exceed a corresponding
angular deviation
threshold, then, or if step (7508) had been bypassed, in step (7512), a heart-
cycle-duration
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deviation of the selected heart beat 82, j is compared with an associated
heart-cycle-duration
deviation threshold, wherein, for example, the heart-cycle-duration deviation
is given by the
difference between the longest beat period T and the heart-cycle period Ti, or
by the
difference between the heart-cycle period Ti and a median value thereof
Otherwise, from step
(7506), if the difference-vector magnitude di exceeds the corresponding
difference-vector
magnitude threshold, or from step (7510), if the vector hyper-angle ai (or the
cosine thereof)
exceeds the corresponding angular deviation threshold, or, from step (7512),
if the heart-cycle-
duration deviation exceeds the heart-cycle-duration deviation threshold, then,
in step (7514), the
selected heart cycle 82, 82.j, b is ignored (or flagged to be ignored). Then,
from either steps
(7514) or (7512), or if step (7512) had been bypassed, then, from step (7516),
if all of the heart
cycles 82, 82.j, b have not been processed, then the heart-cycle pruning
process 7500 is
repeated beginning with step (7502) for the next heart cycle 82, 82.j, b.
Otherwise, from step
(7516), after all of the heart cycles 82, 82.j, b have been processed, the
heart-cycle pruning
process 7500 returns to the point of invocation.
Accordingly, the quality of the segmented heart beats 82, 82.j can then be
assessed in a
counterpart to step (3714) by comparison of the associated vector magnitude
di, the associated
vector hyper-angle aj (or the cosine thereof), and/or the associated
fluctuation in the length of
the heart beat 82, j in relation to that of the ensemble with corresponding
upper bound
threshold values, for example, maximum difference-vector magnitude dma,
maximum
angular deviation a,max (or cosine thereof cos ocma.) and/or maximum tolerable
deviation in the
length of the heart beat 82, j. For example, FIG. 76 illustrates the results
of screening and
pruning the stack of heart beats 82, 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7.
82.8. 82.9. 82.10
illustrated in FIG. 73, using the third-aspect heart-beat quality assessment
process 7400 with
associated thresholds of a.ax = 50 degrees, and with the maximum deviation in
the heartbeat
length of dt/mean(ti) < 20%, resulting in the rejection of noisy heart beats
82.2, 82.7. 82.8.
82.9. 82.10.
The associated threshold values can be defined empirically considering
reproducibility of heart beats 82, j during the recording interval, noise
conditions and number of
beats labelled as being of insufficient quality. A resulting fraction of
rejected beats that appears
to be too high may indicate a serious problem with recording procedure or
ambient room
conditions less than optimal for the test.
More generally, in accordance with another set of embodiments, instead of
determining
and using the median vector xm, the heart-beat quality assessment process 7400
could
determine an use a composite vector that is responsive to the ensemble of
random vectors
xj =
for example, an average vector or a vector responsive to a
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different ranking that that used to generate a median. Furthermore, the
deviation in heartbeat
length could be determined relative to the length of any of the heartbeats
from the longest to the
shortest, or relative to a composite heartbeat length measure, for example, a
mean length or a
median length of the plurality of heart beats 82, 82.1, 82.2, 82.3, 82.4,
82.5, 82.6, 82.7. 82.8.
82.9. 82.10.
Returning to FIGS. 37, following the acquisition of a sufficient number of
sufficiently-
low-noise heart beats 82, following step (3718), the sampled auscultatory
sound data SmI] is
further preprocessed to emphasize acoustic signals in diastole D and extract
signal specific
features that can be used for disease classification. For example, in step
(3722), the mean
to position of the peak of the S2 heart sound ¨ for example, the above-
described mean value ts2 --
is determined for each heart beats 82, after which the heart beats 82 are
synchronized with
respect thereto, for example, as illustrated in FIGS. 39a and 39b, which
respectively illustrate a
stack of heart beats 82 before and after this S2 alignment, wherein the
synchronization of the
heart beats 82 with respect to the S2 heart sound helps to locate an acoustic
signature that might
be present in a majority of the heart beats 82 and that is coherent between or
amongst different
heart beats 82.
Then, in step (3724), the heart beats 82 are normalized with respect to time
so as to
compensate for a variation in heart-beat rate amongst the heart beats 82, and
to compensate for
a resulting associated variation in the temporal length of the associated
diastolic intervals.
Generally, heart-beat rate is always changing and typically never remains the
same over a
recording period of several minutes, which if not compensated, can interfere
with the
identification of specific signal features in diastole D, for example, when
using the below-
described cross-correlation method. Although heart-beat segmentation alone
provides for
aligning heart-beat starting points, variations in the heart-beat rate can
cause remaining features
of the heart cycle 82 to become shifted and out of sync ¨ i.e. offset -- with
respect to each other.
However, such offsets can be removed if the associated heat beats 82 are first
transformed to
common normalized time scale UT*, where 7' is the fixed time interval, for
example, the
duration of the slowest heart beat 82, followed by beat resampling and
interpolation so as to
provide for normalizing the original signal at a new sampling rate, for
example, as illustrated in
FIGS. 40a and 40b that respectively illustrate stacks of heart beats 82 before
and after such a
time normalization, wherein FIG. 40a illustrates complete heart cycles 82, and
FIG. 40b
illustrates only the temporally-normalized diastolic portions D thereof
Referring again to FIG. 37, in step (3730), a final stage of signal pre-
processing provides
for extracting acoustic features from the recorded heart beats 82. There are
numerous options
that can be applied to feature extraction which typically involves certain
transformation of raw
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data to low-dimensional or sparse representation that uniquely characterizes
the given recording.
Alternatively, the raw data can be transformed in a set of images and some
image recognition
algorithm like convolutional neural net can be employed for automatic feature
selection. For
example, in accordance with one set of embodiments, referring to FIG. 41, a
local cross-
correlation (CC) of multiple heart beats 82 provides for identifying a
relatively high-pitch signal
component in the diastolic phase of the heart cycle 82 occurring in multiple
heart beats 82,
which can be result from turbulent blood flow, wherein pairs of heart beats 82
of a 2-D stack of
heart beats 82 ¨ each segmented from the above-described high-pass-filtered
breath-held
sampled auscultatory sound data 72, s []
__________________________________________ are cross-correlated with one
another by computing
to an associated set of cross-correlation functions Rxixi for each pair of
heart beats 82, x1[1, xj[].
This computation is made using a sliding short-time window with NI, samples
(for example,
typically 128) which is advanced in time one sample per each iteration of the
cross-correlation
computation. The cross-correlation is computed without time lag, resulting in
an array -- of the
same size as that of the original signals -- that is given by:
1 -
is R (n) = x (n + k) = x j (n + k)
N k-1 (22)
The cross-correlation assigned to each beat is given by an average of cross-
correlations thereof
with the remaining Nb - 1 heat beats 82:
1 ___________________________ Iv' -1
R (n) = Rõ (n)
(Nb 1) 1=1
(23)
wherein xi and xj are the diastolic high-pass-filtered breath-held sampled
auscultatory sound
20 data 72, s[] of two distinct heart beats 82, and Nb is the total number
of heart beats 82 in the 2-
D stack. Following computation of all possible pairs of heart beats 82, an Nb
x Nt cross-
correlation matrix is obtained and displayed as a 2D image, wherein Nt is the
number of time
samples in each heart beat 82. A similar signal pattern during diastole that
is present in majority
of heart beats 82 will produce a localized cross-correlation peak in diastole.
Accordingly, cross-
25 correlation peaks associated with a micro-bruit signal occurring at
approximately at the same
temporal location within the diastole interval from one heart beat 82 to
another will produce
distinct bands across the image within the same temporal region of each heart
beat 82, for
example, as illustrated in FIG. 42, which illustrates an image of the cross-
correlation function
Rxi as a function of n (corresponding to time) for each heart beat 82, with
different heart beats
30 82 at different ordinate positions, wherein the value of the cross-
correlation function is indicated
by the color of the associated pixels. The cross-correlation operation
provides for emphasizing
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signal features that are coherent within the current time window, and provides
for suppressing
uncorrelated noise, thereby providing for increasing the associated signal-to-
noise ratio.
Alternatively, or additionally, acoustic features in diastolic interval of
heart beat 82 can
be visualized using continuous wavelet transform (CWT). The wavelet transform
processing is
similar to the short-time cross-correlation but instead of cross-correlating
signals from different
heart beats 82, the signal of interest is correlated using a wavelet function
with limited support
to facilitate temporal selectivity.
¨
X (a,b) = 11/2 ¨ x (t)yi b ¨ dt
(24)
\ a
wherein the associated mother wavelet w is a continuous function in time and
frequency
domains. The wavelet family is typically chosen empirically using specifics of
the signal under
consideration. The family of Morlet wavelets appears as an appropriate choice
for analysis of
heart sounds. Output of the wavelet transform is a two-dimensional time-
frequency
representation of signal power 1X(a,b)12 defined in terms of scaling and shift
parameters. An
example of a wavelet transform ¨ using a 6th order Morlet wavelet -- is
illustrated in FIG. 43,
where the associated corresponding original color map represents distribution
of the signal
power in a time-frequency plane, with normalized time-shift ¨ as a function of
b -- as the
abscissa, and frequency as the ordinate, with frequency fin Hertz given as
follows:
f = 21z. _____________ - a
(25)
wherein w is the wavelet order, for example, w=6 for a 6th order Morlet
wavelet, and the results
plotted in FIG. 43 are the result of a cumulative processing of the diastolic
portions of a plurality
of heart cycles. For the purpose of signal classification, the wavelet
representation can be
reduced further by dividing the time axis into discrete intervals and
computing overall signal
power within such interval and specific frequency bandwidth. For example, in
one embodiment,
the wavelet image may be subdivided in time intervals of 200 milliseconds and
two frequency
bands of 10 Hz ¨ 40 Hz and 40 Hz ¨ 100 Hz. The resulting output vector of the
signal power
within the associated intervals of time and frequency can be used as an input
to a neural network
classifier.
Generally, step (3730) of the second-aspect auscultatory sound signal
preprocessing
and screening process 3700 ¨ which may also be used in cooperation with the
above-described
first aspect auscultatory sound signal preprocessing and screening process
2100 --
incorporates one or more feature extraction algorithms which identify
significant signal
parameters that can be linked to coronary artery disease (CAD) and which can
be used for
training a machine learning algorithm for automatic CAD detection.
Furthermore, if
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auscultatory sound signals 16 are recorded at a relatively high sampling rate
(for example, at a
4 kHz sampling rate), each heart beat 82 might contain over 4000 samples per
each of six
channels. Such large amount of highly correlated variables makes the usage of
the raw
waveform for classification very difficult without additional signal
processing to reduce the
dimensionality of the problem. Such dimensionality reduction can be achieved
by use of an
appropriate feature extraction algorithm that identifies a reduced set of
parameters that are
related to CAD. Mathematically, the feature extraction procedure provides a
mapping of the
high-dimensional raw data into the low-dimensional feature space with adequate
inter-class
separation. For example, standard dimensionality reduction routines such as
singular value
to decomposition (SVD) or principal component analysis (PCA) may be used to
decompose raw
data onto orthonormal basis and to provide for selecting relevant features
with minimal loss of
information. The time domain signal itself can be transformed prior to feature
extraction to
emphasize unique features thereof For example, frequency domain representation
by Fourier
transform can be advantageous for feature extraction if the signal contains
discrete set of
characteristic frequencies. The performance of a signal classifier can be
dramatically improved
by excluding a large number of irrelevant features from analysis. In
accordance with one aspect,
the signal classification problem begins with a mapping from the original high-
dimensional
space (size N) to a feature space (size p <<N), followed by a mapping of the
feature space to an
m-dimensional space, wherein the dimension m is equal to the number of
classes. For example,
for a binary classification problem-- e.g. CAD or no CAD, -- m=2.
In accordance with one set of embodiments, step (3730) of the second-aspect
auscultatory sound signal preprocessing and screening process 3700 employs a
wavelet
packet transformation (WPT) for sparse representation of heart sounds in time-
frequency
domain, followed by a custom designed binary classifier. Several standard
classifier algorithms
can be trained using reduced feature set, and to provide for binary
classification of the associated
heart sounds-- useable either individually or in combination, -- including,
but not limited to, a
support vector machine (SVM), a fully-connected artificial neural network
(ANN), or a
convolution neural network (CNN) applied to two-dimensional time-frequency
images.
Referring to FIGS. 57-66, a wavelet packet transformation (WPT) processing
stage
provides for reducing dimensionality by converting raw time-domain
auscultatory sound
signals 16 into a time-frequency basis using discrete wavelet transform (DWT),
followed by
elimination of associated components that do not provide significant
contribution to the original
signal or do not provide substantial contrast between two classes of interest.
For example,
referring to FIG. 57, in one set of embodiments of discrete wavelet
decomposition, an initial
(input) signal x[n] is passed through a series of stages at which low-pass and
high-pass filter
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functions (quadrature mirror filters) are applied to obtain approximation a(k)
and detail di(k)
coefficients at the .fh level of decomposition. This procedure is repeated
recursively at the
subsequent steps with the only approximation coefficients used as an input,
The coefficients
generated at each indicated level of filtering provide the corresponding
amplitude of the initial
signal x[nf after filtering by a filter having a corresponding bandpass filter
response
characteristic illustrated in FIG. 58.
Referring to FIGS. 59 and 60, In accordance with one set of embodiments, the
discrete
wavelet transform (DWT) is implemented using a Daubechies wavelet family, for
example, a
Daubechies 4 (db4) wavelet family, for which the associated scaling function
co -- having low-
pass filter coefficients ho through h7 -- is illustrated in FIG. 59, and for
which the associated
wavelet function
-- having high-pass filter coefficients go through g7 - is illustrated
in FIG.
60.
Referring to FIGS. 57 and 61, at each node 92 of the discrete wavelet
transform (DWT),
an input time series w.bk[// from decomposition level j is transformed into
two output time series
at decomposition level j+1, i.e. wi-F/,241/ and wi-F1,2k-Fifif each containing
half the number of
samples as in the input time series, and mutually-exclusive halves of the
frequency content of the
input time series, wherein the transformation to generate the wi-F/,2k-F1N
lower-half bin of
frequency content is given by the transformation 61.1g¨>61.2g illustrated in
FIG. 61 and in
equation (26) below, and the transformation to generate the wi-E/,24/J upper-
half bin of frequency
content is given by the transformation 61.1h¨>61.2h illustrated in FIG. 61 and
equation (27)
below, wherein k designates an associated frequency bin for the associated
decomposition level
J.
The filter functions are designed to provide for energy conservation and
lossless
reconstruction of the original signal from the set of transformed time series
wi,d/J from a
particular decomposition level]. These properties along with smoothness
requirements define the
family of scaling and wavelet functions used for decomposition. The resulting
set of K=21
distinct frequency bands at each decomposition level j, together with the
corresponding
associated transformed time series wi,j/J. can be used for analysis and
feature extraction, instead
of relying upon the corresponding original raw signal xlid.
The wavelet packet transformation (WPT) is generalization of the standard
multi-level
DWT decomposition, wherein both approximation and detail coefficients are
decomposed using
quadrature mirror filters, for example, as described in M. Wickerhauser, -
Lectures on Wavelet
Packet Algorithms", http://citeseerx.ist.psu.edu, which is incorporated herein
by reference. FIG.
62 illustrates a wavelet packet transformation (WPT) at decomposition level
j=3, and FIG. 63
illustrates the bandpass frequency responses associated with each of the
outputs thereof
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Analytically, the wavelet packet transformation (WPT) decomposition can be
described by the
following recursive expressions:
14, j +1,2 lc +1 [i] = Lgrnivi,, [2.1¨ in]
(26)
m=1
wid-1,2k [1] = [2-1¨m]
(27)
m=1
where g-,,, is the coefficient of the scaling function and 11õ, is the
coefficient of the wavelet
function, k is the index of the associated frequency bin at decomposition
level j, and / is the
index of the associated time series array associated with the particular
frequency bin k.
The wavelet packet transformation (WPT) provides a benefit of sparse signal
representation similar to the discrete wavelet transformation (DWT), but also
provides better
resolution of frequency components by decomposing the detail part of discrete
wavelet
transformation (DWT), which results in the sub-band structure illustrated in
FIG. 63. The
maximum decomposition level j for a given signal depends on the signal length
and the trade-off
between desired time and frequency resolutions. The resulting tree-like
structure of wavelet
packet transformation (WPT) bases ¨ each base corresponding to a particular
frequency bin k at
a particular decomposition level j provides a highly redundant representation
of the original
signal xlnl, which limits the amount of dimensionality reduction. However,
heart sounds have
characteristic properties that can be well described by a relatively small
number of wavelet
packet transformation (WPT) bases and a substantial amount of irrelevant
information can be
discarded. For example, in one set of embodiments, the WPT tree nodes with
highest energy
concentration are retained for further analysis and classification. For
example, FIG. 65
illustrates a wavelet packet transformation (WPT) energy map of the high-pass-
filtered breath-
held sampled auscultatory sound signal 72 for the heart cycle 82 illustrated
in FIG. 64,
generated by an 8-level wavelet packet transformation (WPT) using a Daubechies
4 (db4)
wavelet family, wherein the individual nodes 92 ¨ or associate bases or
frequency bins -- are
outlined with rectangular boundaries in the image, and each frequency bin is
color coded to
indicated the total energy Ejk of the kth node at thefh decomposition level,
which is computed
using wavelet packet transformation (WPT) coefficients wikill as follows:
Elk = Ew.,.k2[i]
(28)
For each decomposition level j, the total energy from all frequency bins is
the same, i.e.
Eratal E is the same value regardless of decomposition level
j. (29)
The wavelet packet transformation (WPT) energy map of FIG. 65 illustrates that
most of
the signal energy is concentrated in the relatively low frequency bands, and
that only the levels 7
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and 8 of decomposition show the fine structure of the energy distribution.
Accordingly, this
example shows that most of the wavelet packet transformation (WPT) nodes 92
can be ignored
without significant loss of information. A formal approach to the selection of
the best bases
that relies on Shannon entropy as a cost function computed for each node in
the tree --is
described by H. Saito and R. Coifman in the following documents that are
incorporated herein by
reference: N. Saito, R.R. Coifman, -On local feature extraction for signal
classification",
https://www.math.ucdavis.edu/ ¨saito/publications/saito iciam95.pdf, and N.
Saito, R.R.
Coifman, "Local discriminant bases and their applications", J. Math. Imaging
and Vision, 5,
337-358 (1995). For example, traversing the wavelet packet transformation
(WPT) tree from the
leaf level up (i.e. highest to lowest decomposition level j), children nodes
with the combined
entropy lower than that of the parent node will be retained as the most
relevant for a given signal,
for example, as illustrated in FIG. 66 for the wavelet packet transformation
(WPT) energy map
illustrated in FIG. 65, wherein the Shannon entropy is given by:
/hi< =IP/,k [i]. 1 g(P),k [/])
(30)
2 ril
and pi [1] = k I-2 . (31)
The Wavelet Packet Transform (WPT) provides a redundant representation of
actual
signal using the discrete wavelet transform with various decomposition levels
that provide higher
frequency resolution (in terms of filter banks) at the expense of reduced time
resolution. The
WPT of the signal can be described by a set of coefficient wj(k, 1), where j
is the decomposition
level, k is the filter bank index and 1 is the coefficient index within filter
bank. An example of
WPT decomposition is shown in FIG. 65 where each cell outlines positions of
filter bank for
each level and intensity is related to the signal energy from equation (28).
It can be seen that
energy distribution withing specific frequency bands becomes apparent at high
decomposition
levels (j > 6). Although, the color of the high frequency cells is dark blue,
the values of the
associated WPT coefficients is not zero.
The entire WPT energy map is highly redundant and not well suited in its raw
form for
signal classification. Instead, only those cell that uniquely characterize a
given signal are kept
while ignoring remaining cells. This can be accomplished using the best WPT
basis selection
algorithm which employs entropy WPT coefficients as a metric of the filter
bank (cell)
importance. This algorithm runs from leave cells up along the decomposition
tree and selects
parent cell whose entropy is less compared to the sum of the two children
cells. An example of
best basis selection applied to the WPT map in FIG. 65 is shown in FIG. 66.
Values the best
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basis would generally not coincide with the energy map shown in FIG. 65
because entropy and
energy differ from one another,
In FIG. 65, the total energy of a particular cell is represented by the
intensity thereof.
FIG. 65 illustrates that most of the energy is concentrated in the relatively-
lower frequency
bands. The cells of FIG. 66 ¨ for which intensity is inversely related to
entropy ¨ are considered
to be a signature of a given signal, and do not overlap with the corresponding
cells of FIG. 65,
the latter of which is representative of energy. None of the cells below the
highlighted (children)
cells in FIG. 66 are selected because they exhibit high entropy.
The wavelet packet transformation (WPT) energy map and the associated best
basis
selection can be used to reduce dimensionality of the heart beat
classification problem by
analyzing the signal represented by transformed time series wi,kt/J and
rejecting information
irrelevant for the classification task. The wavelet packet transformation
(WPT) is one of a
variety of signal processing techniques that can be used for extraction of
important parameters or
features suitable for prediction of CAD. The very basic set of features may
include typical
is metrics of raw signals (amplitude, timing, spectral power and other)
that can be derived from
segmented heart beats. However, such hand-crafted feature set may not be
optimal for current
problem of CAD classification. Regardless of which method is used for feature
extraction, the
output of this data processing stage is a vector ofp elements with p <<N,
where N is the size of
raw signals. The feature vector can be represented either as a 1-D array of p
elements or as a 2-
D matrix for a classification algorithm operating on image information. There
are several
powerful classification algorithms that can be trained for disease detection
using recorded heart
sounds and the extracted feature vector. These algorithms include support
vector machine
(SVM), feed-forward artificial neural network (ANN) and convolutional neural
network (CNN),
which is particularly suitable for 2-D image classification.
Referring to FIG. 67, a support vector machine (SVM) is a powerful supervised
machine
leaning algorithm frequently used for data classification problems, which has
several useful
properties and can operate reliably on a small data sets with poor class
separation. In case of
linearly separable data, the SVM classifier will create decision hyperplane in
feature space with
highest separation between two classes using training data set. For example,
FIG. 67 illustrates
a trained SVM classifier and decision boundary for a two-dimensional feature
space with two
classes. The support vectors are circled, and the highest margin hyperplane is
shown by the solid
straight line. When separation between classes is less then perfect, the
tolerance of SVM
algorithm to data misclassification can be tuned by adjusting an associated C
parameter during
training. In more complicated situations, a nonlinear decision boundary can be
created using a
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kernel function to project data into higher dimensions and apply SVM algorithm
to modified
data.
The SVM algorithm can be used as a CAD classifier with data recorded by the
Data
Recording Application (DRA) 14, 14.1. Prior to sending data to SVM algorithm,
recordings
are processed by beat segmentation and feature extraction stages, to produce a
feature vector for
each test-subject 22, by preprocessing of n available recordings and
extracting p features, with
each channel data then transformed into an n x p feature matrix. The feature
vector extracted
from the segmented beats can be either a set of custom selected metrics
(amplitude, timing of
specific segment, energy, statistic parameters, sample entropy and others) or
a subset of wavelet
packet transformation (WPT) coefficients associated with the signal region of
interest. If the
resulting number of features p is still relatively high, a principal component
analysis (PCA)
procedure can be applied to identify a subset of features with highest
variance and eliminate
correlating features. After all preprocessing steps and data normalization,
the feature matrix is
split into testing and training sets with ratio 1 to 4 respectively. The
training set is used to train
SVM and optimize classifier hyper-parameters, while the testing set is used to
evaluate classifier
performance with unseen data. Computer code that provides for implementing a
SVM classifier
is available in several open source packages for Python and R programming
languages, for
example, the sklearn machine learning package in Python.
Referring to FIG. 68, a feed-forward artificial neural network (ANN) provides
an
alternative option for classification of auscultatory sound signals 16
following preprocessing
and feature extraction stages. An artificial neuron is a nonlinear processing
element with p
inputs and a nonlinear activation function that generates an activation
output. Assuming x(p) is a
feature vector which is sent to the p inputs of each neuron, then the
associated activation output
of Pi neuron is computed as:
(
a, = g EH)I . = x b
J I
(32)
wherein wij is the weight matrix that defines connection strength of id'
neuron tot input, bi is the
neuron bias and g(z = lovTx + b) is the associated nonlinear activation
function. The specific form
of the activation function g is chosen at the design stage, wherein commonly
used functions
include sigmoid, hyperbolic tan and rectified linear unit (ReLu). The network
of interconnected
neurons constitutes the artificial neural network (ANN), which can be capable
of modeling
relatively complicated relationships between the input vector and target class
variables by
adjusting weights and biases during training stage.
Properties of a specific artificial neural network (ANN) implementation are
defined at the
design stage and include: 1) number of hidden layers, 2) number of neurons per
hidden layer, 2)
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type of activation function, 3) learning rate and 4) regularization method to
prevent overfitting.
For example, in one set of embodiments, the artificial neural network (ANN) is
implemented
using the open source TensorFlow deep learning framework, which provides for
setting each of
these parameters. The neuron connection strength is defined by the weight
matrix wii for each
layer which is adjusted during network training and a cost function evaluated
at each training
epoch using available truth labels. The artificial neural network (ANN)
training is accomplished
by a standard back-propagation algorithm using a cross-entropy cost function.
The network
design specifics are determined by the available data since a network with
multiple hidden layers
(deep ANN) can be very powerful but also is prone to overfitting when a small
data set is used.
Therefore, the specific artificial neural network (ANN) architecture is
developed on a trail-and-
error basis, dependent upon the available data and the size of feature vector.
In one set of
embodiments, output layer of the artificial neural network (ANN) for a binary
classifier is
implemented as the softmax function with two-element vector [1, 01 for CAD
positive and [0, 1]
for CAD negative case.
For example, in one set of embodiments, the features used as inputs to either
an ANN
classifier or a SVM classifier are selected from the following: length (time
duration) of: S/,
systole, S2, and diastole; ECG RR peak duration; amplitudes of Si, systole,
S2, and diastole;
ratios of corresponding amplitudes; and energy of spectral bands associated
with Si, systole, S2
and diastole. Other features such statistics (mean, variance, skewness and
kurtosis) of the
parameter distributions can also be used as features for input to either an
ANN classifier or a
SVM classifier.
Referring to FIG. 44, in one set of embodiments, a convolutional neural
network is
employed to analyze cross-correlation images of acoustic signals in order to
provide for
automating the process of acoustic feature extraction. The network input
consists of the three-
dimensional array of cross-correlation data recorded from the left or right
positions on the
thorax 20 of the test-subject 22, and combined into a single array, for
example, representing
three individual channels associated with three different auscultatory sound
sensors 12. The
convolutional neural network (CNN) comprises several convolutional layers that
each use a 5x5
kernel array to scan input data to build a structure of acoustic features with
increasing
complexity. Neuron activation uses rectified linear unit (ReI,U) nonlinearity
to produce output
data. The final stage of the network classifier is a fully connected neural
net with an additional
clinical information (age, gender, blood pressure, etc.) merged with the
acoustic features
identified by CNN The network output is a two-node layer implementing binary
classification
via softmax function applied to the incoming data. The CNN classifier is
trained using 80% of
all available data with binary CAD labels (Yes = 1 and No = 0). The remaining
20% of data is
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randomly selected to test the classifier performance. Reported performance
metrics include
prediction accuracy, sensitivity, specificity, negative prediction value and
positive prediction
value. New patient data are classified by passing through the same pre-
processing stages and
feeding the computed cross-correlation image to CNN classifier.
Convolutional neural networks (CNN) have proved to be very efficient for
prediction and
classification problems especially with large scale problems involving images.
The typical size
of input image data can be quite large, which makes application of standard
feed forward
networks either impractical or even impossible due to huge number of
parameters to be trained.
Convolutional neural networks (CNN) accommodate the size of the problem by
weight sharing
to within small number of neurons comprising a receptive field that is
scanned over the 2-D input.
One benefit of using a convolutional neural network (CNN) for machine learning
problems to
the ability thereof to learn important features directly from data, so as to
provide for bypassing
the feature extraction stage that is used by support vector machine (SVM) and
feed-forward
artificial neural network (ANN) classifiers. A typical convolutional neural
network (CNN
structure consists of one or more convolution and pooling layers that build a
hierarchical
structure of features with increasing complexity. Following convolution and
max pooling, the
extracted features are fed to a fully connected network at the final stage of
convolutional neural
network (CNN) classifier. For example, FIG. 44 illustrates a convolutional
neural network
(CNN) architecture incorporating a single convolution layer. Each cell of the
max pool contains
the maximum value of a corresponding array of cells in the corresponding
convolution layer.
The receptive field is a relatively small 2-D array of neurons (for example, 5
x 5) that is
scanned across the input image while performing an associated cross-
correlation operation. A
relatively small number of connected neurons provides for a relatively small
number of
corresponding weights to be adjusted. The max polling operation provides for
reducing the size
of the input to the associated fully-connected neural network by selecting
pixels with maximum
intensity from the associated convolution layer. Similar convolution and max
polling operations
can be performed multiple times to extract the most significant features
before submitting to an
associated fully-connected neural network for classification. Although a
convolutional neural
network (CNN) can be trained to recognize complicated patterns in 2-D data
sets, this typically
requires large amount of data for efficient generalization and to avoid
overfitting. The
convolutional neural network (CNN) classifier can be applied either directly
to the auscultatory
sound signals 16 (or filtered versions thereof), or to corresponding 2-D
images generated
therefrom, for example, using either a continuous wavelet transform or an
associated
decomposition thereof by wavelet packet transformation (WPT) . For example, in
cooperation
with the extraction of features using wavelet packet transformation ( WPT),
the coefficients of
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Jth level of decomposition can be transformed into a matrix with dimensions
(N/2J) x , where
Nis the size of the time domain signal. Such 2-D data can be used train the
convolutional neural
network (CNN) classifier in order to find any patterns associated with CAD.
This type of
network is more complicated than a standard feed-forward artificial neural
network (ANN), and
utilizes more hyperparameters to be tuned to achieve optimal performance. In
addition to
parameters applicable to an artificial neural network (ANN), the convolutional
neural network
(CNN) design includes specification of the number of convolution layers, the
size of receptive
fields (kernel size), number of channels processed simultaneously, filter
properties,
regularization. After finalization of its design, the convolutional neural
network (CNN) can be
trained using a training data set and then evaluated using an unseen test data
set. For example,
for one set of embodiments, the open-source TensorFlow flexible deep learning
toolkit and API
¨ which provide for building high-performance neural networks for variety of
applications ¨
have been used to design and train the convolutional neural network (CNN) for
detecting CAD.
Referring to FIG. 72, a second embodiment of a convolutional neural network
(CNN)
incorporates a plurality of convolution stages (Cony-1, Conv-2, Conv-3, Conv-4
and Conv-5)
that are interspersed with corresponding associated MaxPool stages, wherein
the output of the
final max pooling stage is fed into a first fully-connected neural network
(FC1) with 128
neurons, the output of which is fed into a second fully-connected neural
network (FC2) with 64
neurons, the output of which is processed by a SoftMax output operation given
by:
, out _____________ -
(33)
e`' +e'2
where zi = wiixi + bi , bi is a bias term, and repeated indexes imply
summation. The result of the
classification (CAD/No CAD) is defined by the highest value of output out,.
The input to the convolutional neural network (CNN) is a series of either
FIGS. 41/42-
sytle convolution images, or a series of FIG. 43-sytle CWT transform images,
with one image
for each of the six different auscultatory sound sensor 12, with each image,
for example, given
by a 100 by 200 array of pixels.
Each of the convolution stages (Cony-1, Conv-2, Conv-3, Conv-4 and Conv-5)
provides
for convolving the array of input elements (or pixels) with an either 3x3 or
5x5 kernel or weight
array, which is scanned across the input image with a stride if one pixel. If
zero padding is used,,
a single kernel generates one feature map with the size as the original image,
but 2D convolution
applied as follows:
Y(n1,n1)=IIx(ki,k2)*w(n1¨lc1,n2¨k2)
(34)
k2
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where x(kl, k2) is the input image, and w is the kernel matrix. To extract a
variety of features,
each convolution layer has multiple kernels that are applied to the input
image. For example, in
FIG. 72, each convolution stages (Cony-1, Conv-2, Conv-3, Conv-4 and Conv-5)
is designated
as P x MxN K=LxL, wherein P is the number of different kernels that are
applied, MxN is the
dimension of the associated image, and LxL is the dimension of the associated
kernel matrix. If
zero padding is not used, the dimension of the output from the convolution
stages (Cony-1,
Conv-2, Conv-3, Conv-4 and Conv-5) is reduced by (L-1). Following convolution,
the max
pooling layer is applied which is a simple mask 2x2 scanned across feature
maps. At each
position only one pixel with highest magnitude is selected which is
essentially a decimation
II) operation that reduces the size of feature map by 2 while selecting
brightest pixels.
It should be understood that the CNN, ANN and SVM classifiers each provide an
independent means of classifying the associated input features in order to
determine whether or
not CAD is likely. These are alternative classification algorithms and a
decision as to which one
to use is typically based on their respective performance, for example,
prediction accuracy.
Alternatively, the different algorithms could be combined to make a single
prediction, for
example, using either a voting scheme or a weighted sum of likelihoods.
The auscultatory coronary-artery-disease detection system 10 can present
various
views of the acoustic data (both unprocessed and processed) that was captured
during the test for
review by a clinician. By reviewing different visualizations of the test
results, the clinician may
strengthen his case for a particular diagnosis, either in agreement with or
disagreement with the
result produced by the system. Additionally, particular visualizations may
help reassure the
patient that the diagnosis is the correct one.
Referring to FIG. 45, the system presents heart/arteries view comprising a
graphical
representation of the heart and coronary artery tree, highlighting the data in
accordance with a
Textual View. By selecting any of the occlusions, the clinician is presented
with the option to
highlight the blockage in either: a Stacked Heartbeat View; a Bruit
Identification View (either
mode); a Bruit Analysis View; or a Receiver Operating Characteristic Curve.
For example, FIG.
45 illustrates test results showing severity of obstructions and zonal
location of each obstruction
within coronary artery tree. The tree diagram also indicates which arteries
are critical, and which
are not, with associated color coding in accordance with the amount of
occlusion, for example,
with the illustrated region of 50 occlusion highlighted in a reddish hue, and
the region of 30%
occlusion highlighted in a yellowish hue.
In accordance with the Textual View, the system presents, in a textual manner,
the
system's interpretation of the acoustic data, including: whether or not the
patient has a clinical
level of CAD; the count of obstructions detected, for each obstruction: the
location (zone) of the
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obstruction; the percentage occlusion of the obstruction; or the type of
blockage (soft plaque,
hard plaque). For each of the items in the view listed above, the system
provides for presenting
an associated confidence level, which indicates how confident the system is of
each specific
element of information presented, or, based on the patient's demographic data
(age, sex, BMI,
medical and family history), the percentage of other patients who have a
similar count, severity,
and position of obstructions. From this view, the clinician may switch to any
other view listed in
this document for more information.
Referring to FIG. 46a, the system presents a Receiver Operating Characteristic
(ROC)
curve to highlight the system's CAD detection algorithm sensitivity as a
function of its false
positive rate (1-specificity). The graph's attributes will be as is standard
for ROC curves ¨
vertical axis representing sensitivity, horizontal axis representing the false
positive rate, with a
45-degree dotted line representing a "coin flip" test (AUC = 0.5).
On this graph, the system will plot the ROC curve and calculate the Area Under
the
Curve (AUC) based on the patient's demographics, and the system's clinical
trial results. The
coordinate that corresponds to the current positivity criterion will be
highlighted. The clinician
will be able to display a modified graph if he commands the system to exclude
specific parts of
the patient's demographic data. The graph may or may not contain gridlines
and/or data points
defining the ROC curve, and it may or may not fill in the AUC.
Alternatively, referring to FIG. 46b, the data may be visualized as a line
graph (with or
without underfill), where true positives, true negatives, false negatives, and
false positives are
displayed, with a line indicating the currently-used cutoff value.
Referring to FIGS. 47 through 53, the system provides for visualizing the
acoustic data
resulting from an analysis of the breath-held auscultatory sound signals 16.1
from the
auscultatory sound sensors 12.
Referring to FIG. 47, the system presents a heartbeat view comprising a
graphical plot of
the systolic and diastolic intervals of each heartbeat captured. The
horizontal axis of the graph
represents captured heartbeats. This representation is a subset of the
acoustic capture time, as
some acoustic data acquired is discarded (poor quality, etc.) during the
system's analysis
process. The duration of the Si and S2 sounds are also highlighted on this
axis.
31)
Generally the vertical axis may comprise acoustic data captured from each of
the
system's sensors ¨ both acoustic and ECG. A correlation procedure is performed
to ensure that
the data captured from each of the system's sensors is aligned to one another.
Because the initial
display of this view may contain many dozens of heartbeats, the clinician has
the option to
highlight a subset of the data on the horizontal axis, and command the system
to zoom into the
selected section. In this way, the clinician can perform a deeper analysis on
one or more sections
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of the acoustic capture data as he so chooses, and explore any discrepancies
between the data
captured by the ECG and acoustic sensors for any particular heartbeat. For
example, FIG. 47
illustrates an example of the Heartbeat View wherein the acoustic view has
been limited by the
clinician to Sensor#3 only, and zoomed in to see only 2 heartbeats, with the
ECG data also
displayed.
Referring to FIG. 48, in accordance with a Stacked Heartbeat View, the system
presents
a graphical plot of the systolic and diastolic intervals of each heartbeat
captured. The horizontal
axis of the graph represents time (in seconds or milliseconds) from the
beginning of the systolic
interval to the end of the diastolic interval. The duration of the Si and S2
sounds are highlighted
to on this axis. The vertical axis is comprised of the acoustic data
captured for each of the
heartbeats, in either ascending or descending order of capture, where each
heartbeat is itself a
graph, with a vertical axis representing intensity. A correlation procedure is
performed to ensure
that the start of the systolic interval for each heartbeat is aligned on x-
axis=0. For each heartbeat,
the system highlights any unexpected acoustic signals captured, as such
signals may be an
indication of an obstruction or other cardiac condition. For example, in FIG.
48, unexpected
acoustic signals are highlighted in red. The clinician has the option to
highlight a subset of the
data on the horizontal axis, and command the system to zoom into the selected
section. In this
way, the clinician can perform a deeper analysis on one or more sections of
the acoustic capture
data as he so chooses, especially so that he may explore more deeply any
unexpected acoustic
signals.
Referring to FIG. 49, in accordance with a Bruit Identification View in a Line
Graphs
with Underfill mode, the system presents a graphical plot of unexpected
acoustic signals
captured during the diastolic interval by means of a line graph with
underfill. The horizontal
axis of the graph represents time (in seconds or milliseconds) from the
beginning of the diastolic
interval to the end of the diastolic interval. The duration of the S2 sound is
also highlighted on
this axis. The vertical axis is comprised of the acoustic data captured for
each of the heartbeats,
in either ascending or descending order of capture, where each heartbeat is
itself a graph, with a
vertical axis representing intensity. A correlation procedure is performed to
ensure that the start
of the diastolic interval for each heartbeat is aligned on x-axis=0. An
underfill is used to visually
highlight deviation from the baseline
For each heartbeat diastole displayed, the system highlights any unexpected
acoustic
signals captured, as such signals may be an indication of an obstruction or
other cardiac
condition. Such signals are highlighted by the height of the line graph, which
represents
intensity. These highlighted areas on the graph allow a clinician to
distinguish low-energy noise
(which may or may not be a sign of non-obstructive CAD) from high-energy noise
(which is a
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likely indicator of obstructive CAD). In this view, it is also easy for a
clinician to understand the
correlation (across heartbeats) of noise, as well as the number and timing of
correlated instances
of noise (which may indicate the number and location of blockages,
respectively). For example,
the data illustrated in FIG. 49 exhibits relatively high intensity noise in
many heartbeats at
approximately t=0.05 seconds into diastole.
The clinician has the option to highlight a subset of the data on the
horizontal axis, and
command the system to zoom into the selected section. In this way, the
clinician can perform a
deeper analysis on one or more sections of the acoustic capture data as he so
chooses, especially
so that he may explore more deeply any unexpected acoustic signals.
Referring to FIG. 50, in accordance with a Bruit Identification View in a
Spectrogram
mode, the system presents a graphical plot of unexpected acoustic signals
captured during the
diastolic interval by means of a spectrogram. The horizontal axis of the graph
represents time (in
seconds or milliseconds) from the beginning of the diastolic interval to the
end of the diastolic
interval. The duration of the S2 sound is also highlighted on this axis. The
vertical axis is
comprised of the acoustic data captured for each of the heartbeats, in either
ascending or
descending order of capture. A correlation procedure is performed to ensure
that the start of the
diastolic interval for each heartbeat is aligned on x-axis=0.
For each diastole displayed, the system highlights any unexpected acoustic
signals
captured, as such signals may be an indication of an obstruction or other
cardiac condition. Such
highlighted areas indicate the intensity of the unexpected signal. Highlights
are in the form of
color, which indicate varying intensity of the signal. This view could
alternatively be
represented in monochrome as a contour plot. For example, the data illustrated
in FIG. 50
exhibits relatively high intensity noise in many heartbeats at approximately
t=0.05 seconds into
diastole.
These highlighted areas on the graph allow a clinician to distinguish low-
energy noise
(which may or may not be a sign of non-obstructive coronary artery disease
(CAD() from high-
energy noise (which is a likely indicator of obstructive CAD). In this view,
it is also easy for a
clinician to understand the correlation (across heartbeats) of noise, as well
as the number and
timing of correlated instances of noise (which may indicate the number and
location of
31) blockages, respectively).
The clinician has the option to highlight a subset of the data on the
horizontal axis, and
command the system to zoom into the selected section. In this way, the
clinician can perform a
deeper analysis on one or more sections of the acoustic capture data as he so
chooses, especially
so that he may explore more deeply any unexpected acoustic signals.
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Referring to FIG. 51, in accordance with a Bruit Analysis View, the system
presents a
graphical time/frequency plot of unexpected acoustic signals captured. The
horizontal axis of the
graph represents time (in seconds or milliseconds) from the beginning of the
systolic interval to
the end of the diastolic interval. The duration of the Si and S2 sounds are
highlighted on this
axis. The vertical axis represents the frequency (in Hz or kHz) of any
unexpected acoustic
signals, averaged from all captured data. For each unexpected acoustic signal
captured, the color
on the graph represents the intensity of that signal. Alternatively, the graph
could be represented
in monochrome as a contour plot.
These highlighted areas on the graph allow a clinician to distinguish low-
energy noise
(which may or may not be a sign of non-obstructive CAD) from high-energy noise
(which is a
likely indicator of obstructive CAD). It is also easy for a clinician to
understand the frequency
of high-energy noise, as well as the timing of this noise ¨ this is important
as different cardiac
conditions may be defined by specific frequencies and timings of noise. For
example, the data
illustrated in FIG. 51 exhibits relatively high intensity noises at
approximately t=0.25, 0.75 and
0.8 seconds into the heartbeat.
The system provides a User Interface, and associated navigation, is designed
for use on
tablets and smartphones, and thus uses common touch-screen user interface
paradigms. For
example, two fingers moving apart from one another can be used to zoom in to
any area on any
graph, and two fingers moving together can be used to zoom out. A single
finger can be used to
highlight any areas on the horizontal axis, and the graph can be zoomed in to
that highlighted
area by touching a button.
Touching any area of the graph provides information to the user (either in a
pop-up
window or beside/below the graph) on the values of the horizontal and vertical
axes at that point,
as well as the "height" information of that point if available (e.g. in the
Matrix View). For
example, in Stacked Heartbeat View, touching on an individual heartbeat would
cause the
system to provide the heartbeat number, the maximum intensity of that
heartbeat (and the time
on the horizontal axis at which the maximum intensity occurs), and the time on
the horizontal
axis corresponding to the touch. In the case of the Matrix View, touching on
any area of the
graph would cause the system to provide the frequency, time, and intensity
corresponding to the
coordinate on the graph that was touched
For use with a mouse, the user interface paradigms are similar, except with
zoom. In this
case, zoom can be accomplished through a common desktop/laptop user interface
paradigm, such
as dedicated zoom in/out buttons in the UI, or mouse wheel scrolling.
For some of these graphs, it is possible to restrict the display of data to
some subset of the
acoustic sensors (for example, as illustrated in FIG. 47), to combine acoustic
data from all
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sensors (the default mode), or to display data from each sensor individually.
This may be helpful
to clinicians who want to spatially localize any unexpected acoustic signals.
This capability is
relevant to the Stacked Heartbeat View, the Bruit Identification View (both
modes), and the
Bruit Analysis View.
The following features of sound propagation with the body provide for the
localization of
acoustic sources based upon the relative strengths of the associated acoustic
signals from
different auscultatory sound sensors 12. First, any sound feature that
originates from the heart,
acts as a single source for all auscultatory sound sensors 12. For example,
the characteristic
sound that the heart makes, dub-blub, acts like two sounds coming from two
separate locations.
Second, for practical purposes, sound travels fast enough in the body that all
auscultatory sound
sensors 12 would effectively receive each sound individually at substantially
the same time.
Third, the farther a sound's origin is from an auscultatory sound sensor 12,
the weaker that
sound will be when received by that auscultatory sound sensor 12. This is due
to the sound
energy being dissipated as it travels through the body. In view of these
features, using the
relative signal strengths from different auscultatory sound sensors 12, the
location of the sound
source can be triangulated from the locations of the auscultatory sound
sensors 12 with the
three largest signal strengths, weighted by the relative strengths of those
signals, with the
resulting calculated location of the sound source being relatively closer to
auscultatory sound
sensors 12 with relatively stronger signals than to auscultatory sound sensors
12 with
relatively weaker signals.
More particularly, referring to FIG. 69, in accordance with one method of
localizing the
sound source 94 of an auscultatory sound signal 16 based upon associated
respective signal
strengths Pl, P2 and P3 of respective first 12.1, second 12.2 and third 12.3
auscultatory
sound sensors that are assumed to be located in Cartesian space on an X-Y
plane 96 at
respective predetermined sensor locations (Xi, Y1), (X2, Y2) and (X3, Y3), at
Z=0.
Assuming that the distance from each of the auscultatory sound sensors 12.1,
12.2, 12.3 is
inversely related to the corresponding associated signal strengths Pl, P2 and
P3, the
corresponding lengths Al, A2, A3 of corresponding edges El, E2, E3 of an
associated
tetrahedral solid 98 are assumed to be inversely related to the corresponding
associated signal
strength P1, P2, P3 of the auscultatory sound sensors 12.1, 12.2, 12.3 located
at a
corresponding vertices V1, V2, V3 of the tetrahedral solid 98 to which the
corresponding edge
El, E2, E3 is connected. Other than being sufficiently long to meet at a
fourth vertex V4 of the
tetrahedral solid 98 at location (X, Y, Z), the lengths Al, A2, A3 of the
edges El, E2, E3 are
otherwise arbitrary, although relatively shorter lengths provide for less
geometric dilution of
precision (GDOP) of the ultimate determination of the lateral, (X, Y) location
of the fourth
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vertex V4 than to relatively longer lengths. In view of the lateral (X, 17)
location of the sound
source 94 on the X-Y plane 96 being the same as the lateral (X, Y) location of
the fourth
vertex V4, the lateral (X, Y) location of the sound source 94 may be
determined by solving the
following system of three equations in three unknowns (X, Y, Z) for the
location (X, Y, Z) of
the fourth vertex V4:
(X ¨X1)2 +(Y ¨ Y1)2 +Z2 = Al 2
(35)
(X ¨X2)2 +(Y ¨Y2)2 +Z2 =A22
(36)
(X ¨X3)2 +(Y ¨Y3)2 +Z2 = A32
(37)
Following the solution of equations (35-3 7), the resulting lateral (X, Y)
location of the
sound source 94 may then be displayed, for example, as a location on a
silhouette of a torso, or
transformed to a corresponding location on the image of the heart illustrated
in FIG. 45.
For some of these graphs, it is possible for the user to directly compare the
results of the
current test with one or more previous tests. This can be done by simply
placing the graphs side-
by-side (for example, as illustrated in FIGS. 52 and 53), or by overlaying
them on-top of one
other, with some variable transparency or color variation so that the
clinician can distinguish data
from one test versus another. In the case where the graphs are placed on top
of one another, the
system could fade in to one graph and out from the other slowly, repeatedly in
a loop (almost
like a video file), so that the clinician can easily see the differences in
the processed data between
one test and the other. Alternatively, if the graphs are placed on top of one
another, a subtraction
operation could be performed to highlight only the differences between the two
graphs (e.g. red
for increase in noise from previous test result, green for decrease). For
example, FIG. 52
illustrates a comparison of a current test with a previous test, using the
Bruit Identification View,
Spectrogram mode, to confirm success of PCI. Similarly, as another example,
FIG. 53
illustrates a comparison of a current test with a previous test, using the
Bruit Identification View,
Line Graph with Underfill mode, to confirm success of PCI.
The ability to compare the current test with a previous test is critical to
the clinicians
understanding of the progression of a particular cardiovascular condition
(either worsening, or
improved as in the case of a PCI procedure). This capability is relevant to
the Stacked Heartbeat
View, the Bruit Identification View (both modes), and the Bruit Analysis View.
The graphs may be rendered locally or remotely (server-based) or both,
depending on the
capabilities desired by the clinician and the organization to which he
belongs. In most use cases
(tablet, phone, desktop, or laptop), the graph rendering will be done locally,
either through a
web-browser (full or embedded) on the client, or through a graphics library
optimized for each
specific supported client platform.
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In other cases, the rendering may be done on the server side ¨ graphs may be
generated
and exported to JPEG (or other similar) format so that they can be emailed or
sent via instant
message to interested parties.
Referring to FIG. 77, an auscultatory sound signal 16 associated with a heart
sound
from a single heart cycle may be subdivided into intervals associated with the
Si, S2, S3 and S4
heart sounds, and other intervals, as follows:
1) The Si interval is associated with the first heart sound, Si, resulting
from the closure
of the mitral and tricuspid valves, extends between Sl_start and Sl_end,
wherein Sl_start is
assumed to be proximally coincident with the peak of the ECG R-wave at time 0
of the heart
cycle, and Si_end is assumed to be in accordance with the above-described
heart-cycle
segmentation and heart-phase identification process 2300 illustrated in FIG.
23 et seq, i.e.
t(ksi_END);
2) The S2 interval is associated with the second heart sound, S2, resulting
from the
closure of the aortic and pulmonary valves, extends between S2_start and
S2_end, wherein
S2_start and S2_end are assumed to be in accordance with the above-described
heart-cycle
segmentation and heart-phase identification process 2300 illustrated in FIG.
23 et seq, i.e.
t(kS2_START) and t(ksz_END);
3) The S3 interval is associated with a third heart sound, S3 -- an extra
heart sound
present in some people, but rarely, -- that extends between S3_start and
S3_end, wherein, in
accordance with one set of embodiments, S3_start is assumed to be 60
milliseconds prior to the
Weissler point defined hereinabove by equation (14), and S3_end is assumed to
be 60
milliseconds after the Weissler point;
4) The S4 interval is associated with a fourth heart sound, S4 -- an extra
heart sound
present in some people, but rarely, -- that extends between S4_start and
S4_end, wherein, in
accordance with one set of embodiments, S4_start is assumed to be at 80% of
the beat length,
and S4_end is assumed to be at the beginning of the next heart cycle 82;
5) The systolic interval is associated with the contraction of the myocardium
of the left
and right atria after refilling with blood, that extends between S l_start and
S2_start;
6) The pre-S3 interval that extends between S2_start to S3_start; and
7) The diastasis interval that extends between the Weissler point and S4_start
Referring to FIGS. 78-83, an S3-detection process 8300 illustrated in FIG. 83
provides
for detecting the presence of an S3 heart sound 100 using a k-means clustering
model 102 that
is trained in accordance with the S3-detection-training process 7800
illustrated in FIG. 78. An
unsupervised classification method is used to detect the S3 heart sound 100
because there are
typically relatively few test-subjects 22 that exhibit the S3 heart sound 100
¨ i.e. relatively few
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labeled cases ¨ that could otherwise be used to train a supervised
classification model. For
example, k-means clustering ¨ for example, as described in
,hdpsthren.wikipedia.orgilvikiik-
means clustering, which is incorporated herein by reference -- is an
unsupervised classification
method which, when trained in accordance with the S3-detection-training
process 7600, has
been found to be useful for detecting an S3 heart sound 100 in accordance with
the below-
described S3-detection process 8300.
Referring to FIG. 78, the S3-detection-training process 7800 commences in step
(7802)
with execution of a k-means-clustering training process 7900 that provides for
segregating
heart-cycle data -- from a plurality of heart cycle 82, b recorded by one or
more auscultatory
sound sensor 12, m, for each of a plurality of test-subjects 22, p exclusively
of an associated
training set of test-subjects 22, {P}TRAINTING -- into two different clusters,
i.e. Cluster_O and
Cluster_l, neither of which is known a priori to be associated with an S3-
sound.
Referring to FIG. 79, the k-means-clustering training process 7900 commences
with
step (7902), for each of a plurality of test-subjects 22, p, with the
determination from step
(7904), by an associated S3-clustering feature determination process 8000, of
a set of
features 101 responsive to which the heart-cycle data will be clustered, for
example, in one set
of embodiments, features including an S3swING value, and, for each of a
plurality of frequency
intervals k, a median value of a Short Time Fourier Transform (STFT),
Median_STFTH, and a
mean value of the Short Time Fourier Transform (STFT), Mean_STFTI].
Referring to FIG. 80, the S3-clustering feature determination process 8000
commences, in step (8002), with receipt of a breath-held sampled auscultatory
sound data
SmI] containing a plurality of heart cycles 82 (counted by index b) for one of
a plurality of test-
subjects 22 (counted by index p) from one or more auscultatory sound sensors
12, 121', 122',
123', 121", 122", 123" (counted by index m). Then, in step (8004), breath-held
sampled
auscultatory sound data SmIl is preprocessed with a band pass filter -- for
example, in
accordance with the auscultatory sound signal acquisition and filtering
process 2200
illustrated in FIG. 22a ¨ which uses aa Savitzky-Golay (SG) smoothing filter
68 to extract
low-frequency content, and then removes that low-frequency content by
subtraction after low-
pass filtering using a 4th order type II Chebyshev filer. The resulting band-
pass filtered signal is
then segmented using the above-described heart-cycle segmentation and heart-
phase
identification process 2300 illustrated in FIG. 23 et seq, for example,
responsive to R-peaks
80' of an associated electrographic signal 37. Beginning with step (8008), for
each heart cycle
82, b, in step (8010), the S3 region is located within the interval in
accordance with the above-
defined S3_start and S3_end time/sample locations within the heart cycle 82,
b, after which, in
step (8012), the maximum S3mAx and minimum S3m-IN values within the S3
interval of the
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band-pass-filtered breath-held sampled auscultatory sound data Smil is
determined, after
which an associated swing value S3swiNG is determined as the absolute value of
the difference
between the maximum S3mAx and minimum S3mix values, and stored as a value
S3swiNcip, m,
b] in an associated array. Then, beginning with step (8014), for each jth
window of Nw samples
- for example, 400 samples sampled at 4 KHz, -- of a total of J sliding,
Chebyshev windows, a
Short-Time Fourier Transform (STFT) is calculated in step (8016), for example,
having a 1 Hz
frequency resolution and a 2.5 millisecond time resolution, after which, from
step (8018), step
(8016) is repeated until all J windows of the S3 interval have been processed.
Then, in step
(8020), for each frequency interval k, both the median and mean value of the
STFT results
from step (8018) are calculated with respect to a combination of time windows
and SFTF sliding
windows, resulting a value for each of the median and mean for the test-
subject 22, p,
auscultatory sound sensor 12, m, heart cycle 82, b, and frequency interval k.
Then, from
step (8022), the processes of steps (8008) through (8020) is repeated for each
heart cycle 82, b,
and then, from step (8024), the processes of steps (8002) through (8022) is
repeated for each
auscultatory sound sensor 12, m, after which, in step (8026), the resulting
set of features 101
is returned to step (7904) of the k-means-clustering training process 7900,
after which, in step
(7906), if additional test-subject 22, p remain to be processed, steps (7902)
and (7904) are
repeated for the next test-subject 22, p. Otherwise, from step (7906), in step
(7908),
normalized values of the sets of features 101 from step (7904), i.e.
S3swiNcIp, m, hi,
Median_STFTIp, m, b, k], Mean_STFTIp, m, b, k]: for all p, in, b, k, is
clustered with respect
to S3swiNG, Median_STFT, Mean_STFT, with the resulting k-means clustering
model 102
having two clusters, i.e. Cluster_O and Cluster_l, wherein the normalization
is given by
subtracting an associated mean value and dividing by an associated standard
deviation, after
which, in step (7910), the k-means-clustering training process 7900 returns to
step (7802) of
the S3-detection-training process 7800.
Referring again to FIG. 78, the 53-detection-training process 7800 then
continues with
the characterization in steps (7804) through (7818) of the two clusters,
Cluster_O and
Cluster_l of the k-means clustering model 102, with respect to the S3 sound.
More
particularly, in step (7804), the elements of a counter array, i.e.
S3_Count[0] and S3_Count[1],
are each initialized to a value of zero. Then, beginning with step (7806), for
each heart cycle
82, b of each test-subject 22, p, if, in step (7808), that particular heart
cycle 82, b is known a
priori by other means to include an S3 sound, then, in step (7810), the
cluster c (i.e. c=0 or 1)
into which that heart cycle 82, b for that test-subject 22, p had been
classified in the k-means
clustering model 102 is identified, and, in step (7812), the corresponding
associated counter, i.e.
S3_Count[c], is incremented. Then, or otherwise from step (7808), from step
(7814), the
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process of steps (7806) though (7812) is repeated for each heart cycle 82, b
and each test-
subject 22, p. Then, in step (7816), the cluster, Cluster_O and Cluster_l, for
which the
corresponding associated counter S3_Count[0] and S3_Count[1] has a
substantially larger value
is identified as the S3 cluster C3 associated with the S3 sound. If, in step
(7818), the values of
the counters S3_Count[0] and S3_Count[1] are substantially different -
indicating a strong
association of the S3 cluster C3 with the S3 sound, then, in step (7820), the
S3 cluster C3 is
marked as being valid for use by the S3-detection process 8300. Otherwise,
from step (7818)õ
in step (7822), the S3 cluster C3 is indicated either as being invalid or
otherwise not available
for S3 detection.
For example, for a particular heart cycle 82, b, FIG. 81a illustrates the
associated
electrographic data 74 of the associated electrographic signal 37, FIGS. 81b
and 81c
illustrates a breath-held sampled auscultatory sound data Sm[] prior to and
following,
respectively, removal of the low-frequency content extracted by the associated
Savitzky-Golay
(SG) smoothing filter 68, and FIG. 81d illustrates an example of an associated
Short Time
Fourier Transform (STFT) of the band-pass-filtered auscultatory sound signal
illustrated in FIG.
81c. Furthermore, FIG. 82 illustrates the results of the k-means clustering of
a set of breath-
held sampled auscultatory sound data SmI] for which some of the associated
test-subjects 22
exhibited an S3 sound.
Referring to FIG. 83, following the identification of an S3 cluster C3 by the
S3-
detection-training process 7800, the S3-detection process 8300 provides for
detecting whether
or not a particular test-subject 22, pEvAL exhibits an S3 sound. Following
identification or input
of the test-subject 22, PEVAL in step (8302), in step (8304), the k-means-
clustering training
process 7900 is repeated for an augmented set of test-subject 22, PEVAL
comprising the test-
subject 22, PENAL to be evaluated in combination with the training set of test-
subjects 22,
IpITRAINING that had been used to identify the S3 cluster C3, so as to provide
for associated the
data from the test-subject 22, pEvAL with the clusters, i.e. Cluster_O and
Cluster_l with which
the S3 cluster C3 is associated. Then, for each of the heart cycles 82, b of
the test-subject 22,
pEvAL to be evaluated , the percentage S3% of heart cycles 82, b that are
associated with the S3
cluster C3 is determined in step (8306), and if, in step (8308), that
percentage exceed a
corresponding threshold, for example, 33%, then, in step (8310), an associated
flag
S3_Presentlid for that test-subject 22, p is set to TRUE. Otherwise, in step
(8312), the
associated flag S3_Present[pl for that test-subject 22, p is set to FALSE.
Alternatively, the
test-subject 22, pEVAL to be evaluated could be embedded within the training
set of test-
subjects 22, {141-RAINING, thereby precluding the need for step (8304) of the
S3-detection-
training process 7800 because heart data of the test-subject 22, pEATAL will
already have been
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clustered, so that the presence, or not, of an S3 sound in the test-subject
22, pEvAL, -- or of any
other test-subject 22, p of the training set of test-subjects 22, IP TRAINING,
-- can be
ascertained directly from steps (8306) and (8308) without reprocessing the
data in step (8304).
Referring to FIGS. 84-87, an S4 detection process 8700 illustrated in FIG. 87
provides
for detecting whether or not a particular test-subject 22, p exhibits an S4
sound 104, based upon
a value of S4S2swiNG - determined by an associated S4S2swiNG determination
process 8500
illustrated in FIG. 85 in respect of associated time intervals with a heart
cycle 82, b illustrated
in FIGS 86a and 86b - in comparison with a mean value S4S2swiNG_mean of
S4S2swiNG
across all heart cycles 82, b measured by all auscultatory sound sensors 12 m
from all test-
subjects 22, p as determined by the S4S2swiNG_mean determination process 8400
illustrated
in FIG. 84, which utilizes the S4S2swiNG determination process 8500 to
determine the
underlying values of S4S2swiNG for each heart cycle 82, b measured by each
auscultatory
sound sensor 12, m for each test-subject 22, p.
More particularly, referring to FIG. 84, the S4S2swiNG_mean determination
process
8400 commences with step (8402) for each of a plurality of test-subjects 22, p
of a training set
of test-subjects 22, {p}TRAINING, the latter of which includes a first subset
of test-subjects 22, p
known a priori to exhibit the S4 sound 104, and includes a second subset of
test-subjects 22, p
known a priori to not exhibit an S4 sound 104. Then, in step (8404), breath-
held sampled
auscultatory sound data Sm[] containing a plurality of heart cycles 82, b for
the selected test-
subject 22, p is received from one or more auscultatory sound sensors 12,
121', 122', 123', 121",
122", 123" (counted by index m), and in step (8406), the associated values of
S4S2swiNG
[p] [m] UN] are determined by the associated S4S2swiNG determination process
8500 for each
of the heart cycles 82, b, wherein braces -{ -I" are used herein to indicate a
plurality of values of
an associated 1-dimensional vector. Then, from step (8408), the process of
steps (8402) through
(8408) is repeated form each of test-subjects 22, p and associated
auscultatory sound sensors
12 m, after which, in step (8410), the mean value S4S2swING_mean of S4S2swiNG
of all heart
cycles 82, b from all auscultatory sound sensors 12 m for all test-subjects
22, p is calculated.
Referring to FIG. 85, the S4S2swiNG determination process 8500 commences in
step
(8502) with receipt of the breath-held sampled auscultatory sound data Sm[]
for a particular
auscultatory sound sensor 12, m of a particular test-subject 22, p that is
passed on from either
step (8404), or below-described step (8702). Then, in step (8504), the breath-
held sampled
auscultatory sound data Sul] is segmented into separate heart cycles 82, b
responsive to
locations of the R-peaks 80' of the associated electrographic data 74 of the
associated
electrographic signal 37, wherein a particular heart cycle 82, b is assumed to
start at one R-
peak 80' and end at the next R-peak 80'. Then, beginning with step (8506), for
each heart
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cycle 82, b, with the heart cycle 82, b starting at time 0, the heart cycle
duration ATR_R ms in
milliseconds is given in step (8508) as the relative time of the next R-peak
80'. Then, in step
(8510), the Weissler point DTms is then calculated from equation (14)
hereinabove, and an
associated midpoint Tmip is given as the midpoint in time between the start of
the heart cycle
82, b and the Weissler point DTms, i.e. Tim]) = DTms /2. Then, in step (8512),
the start and end
of each of the S2 and S4 intervals are defined for purposes of determining
associated swing
values as follows:
Ts2_sTART = TMID, TS2_END = DTms, T54 START = 0.8 * ATR-R_ms; and TS4_END =
ATR-R_ms=
The rationale for these locations is that R-peak 80' provides the best
estimate of the S4 end point
and avoids leakage of the Si sound into the S4 sound. The start of the S4
sound is synchronized
with the P-wave of the ECG signal, which is not always apparent, so that the
beginning of the
last 20% of the heart cycle 82, b is used instead. The use of TMID and DTms
for the bounds of
the S2 interval is sufficient to provide for determining the associated
minimum and maximum
values that occur therebeween. Following the definition of the S2 and S4
intervals in step
(8512), in step (8514), maximum and minimum values of each of S2 and S4 are
determined
within their respective intervals, and the associated swing values S2swiNG and
S4swiiNG are
respectively given by the absolute values of the corresponding differences
between the
corresponding associated maximum and minimum values within the corresponding
associated
intervals, respectively. Then, in step (8516), the corresponding associated
ratio of swing values,
i.e. S4S2swIING[b] for the particular heart cycle 82, b is determined from the
ratio of S4swIING
over S2swiNG, and saved as an element of the array S4S2swiNG[b]. Then, from
step (8518), the
process of steps (8506) through (8518) is repeated for each heart cycle 82, b,
followed by a
return in step (8520) of the S4S2swiNGI{b}] array.
Referring to FIG. 87, the S4 detection process 8700 commences in step (8702)
with
receipt of breath-held sampled auscultatory sound data Sm 1] containing a
plurality of heart
cycles 82, b from one or more auscultatory sound sensors m, 12, 121', 122',
123', 121", 122",
123" for a test-subject 22, p to be evaluated, after which, in step (8704),
values of a
corresponding associated S4S2swiNG[m] Uhl] array are determined for each of
the auscultatory
sound sensors 12 m. Then, in step (8706), and associated counter S4count is
initialized to
zero, after which, beginning with step (8708), for each heart cycle 82, b of
each auscultatory
sound sensor 12, m, from step (8710), if the corresponding value of
S4S2swiNG[m][b] is greater
than the mean value S4S2swING_mean of S4S2swiNG of all heart cycles 82, b from
all
auscultatory sound sensors 12 m for all test-subjects 22, p determined from
step (8410), then,
in step (8712), the counter S4count is incremented. Then, or otherwise from
step (8710), in
step (8714), the process of steps (8708) through (8714) is repeated for all
heart cycles 82, b
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and auscultatory sound sensors 12, m, after which, in step (8716), if counter
S4count as a
fraction of the total number of combinations of heart cycle 82, b and
auscultatory sound
sensor 12, m is greater than a threshold, then, in step (8718), the flag
S4_Present[p] is set to
TRUE for the test-subject 22, p so as to indicate the presence of an S4 sound,
otherwise, in step
(8720), the flag S4_Present[p] is set to FALSE for the test-subject 22, p so
as to indicate the
lack of an S4 sound.
Referring to FIGS. 88-93, a first aspect of a CAD detection process 9300
illustrated in
FIG. 93 provides for determining whether or not a particular test-subject 22,
p is likely to
exhibit coronary artery disease (CAD) responsive to parameters of a test-
subject 22, p and
responsive to swing measures determined from associated breath-held sampled
auscultatory
sound data Smil from one or more auscultatory sound sensors 12, 121', 122',
123', 121", 122",
123", responsive to a Support Vector Machine (SVM) 106 that is trained by an
associated
SVM training process 8800 illustrated in FIG. 88, in accordance with a
supervised machine-
learning process, wherein both the SVM training process 8800 and the CAD
detection process
9300 utilize an executive swing-measure determination process, 8900
illustrated in FIG. 89,
to determine swing measures associated with a plurality of frequency ranges f,
and the swing
measures associated with a particular frequency range f are determined with a
kernel swing-
measure determination process, 9000 illustrated in FIG. 90 that provides for
optionally
utilizing one or more supplemental swing measures {X], for example, in
accordance with FIGS.
91 and 92.
More particularly, referring to FIG. 88, the SVM training process 8800
commences
with step (8802) for each of a plurality of test-subjects 22, p of a training
set of test-subjects
22, {pIrRAINING, the latter of which includes a first subset of test-subjects
22, p known a priori
to exhibit coronary artery disease (CAD), and includes a second subset of test-
subjects 22, p
known a priori to not exhibit coronary artery disease (CAD). Then, in step
(8804) various
disease-relevant parameters of the test-subject 22, p are identified,
including, inter alia, age
(which is stored in an array Age[p]), sex (male or female) and their CAD
status, i.e. known to
exhibit exhibit coronary artery disease (CAD) or not. Then, from step (8806),
if the test-subject
22, p exhibits an S4 sound 104 responsive to the S4 detection process 8700,
then, in step
(8808), an 5465 parameter is set to a value of 1 for that test-subject 22, p
to indicate the
present of an S4 sound 104. Otherwise from step (8806), if, from step (8810),
the age of the
test-subject 22, p is greater than 65, then, in step (8812), the S465
parameter is set to a value
of -1 for that test-subject 22, p to indicate that there is no S4 sound 104,
and there also may be
no coronary artery disease (CAD). Othenvise, from step (8810), in step (8814),
the S465
parameter is set to a value of 0 for that test-subject 22, p to indicate that
there is no S4 sound
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104. Then, from steps (8808), (8812), or (8814), if, in step (8816), the test-
subject 22, p is
male, then, in step (8818), an IsMale parameter is set to TRUE for that test-
subject 22, p,
otherwise, in step (8820), the IsMale parameter is set to FALSE. Then, from
step (8822), if the
test-subject 22, p is known a priori to exhibit coronary artery disease (CAD),
then, in step
(8824), a CAD_Status parameter is set to TRUE for that test-subject 22, p,
otherwise, in step
(8826), the CAD_Status parameter is set to FALSE. Then, in step (8828), breath-
held
sampled auscultatory sound data SmIl containing a plurality of heart cycles
82, b for the
selected test-subject 22, p is received from one or more auscultatory sound
sensors 12, 121',
122', 123', 121", 122", 123" (counted by index m), and in step (8830), a
plurality of swing
to measures are determined therefrom by the below-described executive swing-
measure
determination process, 8900, after which, from step (8832), the process of
steps (8802)
through (8832) is repeated for the next test-subject 22, p. Following step
(8832), after all test-
subjects 22, p of the training set have been processed, then, in step (8834),
the associated
Support Vector Machine (SVM) 106 is trained in accordance with a supervised
machine
learning process to distinguish between conditions with and without coronary
artery disease
(CAD) using inputs of S4 Present, Age, S465, IsMale, CAD_Status, swing
measures, and
possibly one or more supplemental swing measures, for each of the heart cycles
b, each of the
frequency ranges f, each of the auscultatory sound sensors 12 m, and each of
the test-
subjects 22, p, In one set of embodiments, about 70% of the test-subjects 22,
p are randomly
picked for use in training the Support Vector Machine (SVM) 106, with the
remainder used to
test the Support Vector Machine (SVM) 106. Alternatively, others type of
supervised machine
learning systems could be used instead of, or in combination with, a Support
Vector Machine
(SVM) 106, including, but not limited to, LR (Logistic Regression) or RF
(Random Forest).
Referring to FIG. 89, the above-referenced executive swing-measure
determination
process, 8900 commences in step (8902), for each of the associated
auscultatory sound sensors
12 m, with, in step (8904), receipt of the associated breath-held sampled
auscultatory sound
data Sul] for the associated test-subject 22, p. Then, in step (8906), the
swing measures are
determined by the kernel swing-measure determination process, 9000 for a first
range of
frequencies, for example for an infrasound range of frequencies, for example,
between fO_low=1
Hz and fO_high=20 Hz Then, in step (8908), the swing measures are determined
by the kernel
swing-measure determination process, 9000 for a second range of frequencies,
for example for
an audible range of frequencies, for example, between a lower frequency bound
fO_low of 20
to 25 Hz, and an upper frequency bound fl_high of 30 to 40 Hz. Then, from step
(8910), the
process of steps (8902) through (8910) is repeated until all of the
auscultatory sound sensors
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12 m have been processed for a given test-subject 22, p, after which, in step
(8912), the
resulting array(s) of swing measures are returned to the point of invocation.
Referring to FIG. 90, the kernel swing-measure determination process, 9000
commences in step (9002) with receipt of the associated breath-held sampled
auscultatory
sound data SmI] for a particular frequency range f, from a particular
auscultatory sound
sensor 12, m, for the associated test-subject 22, p. Then, in step (9004), the
breath-held
sampled auscultatory sound data SmI] is bandpass filtered using a third order
Butterworth filter
with associated cutoff frequencies in accordance with the indicated frequency
range. The
resulting filtered breath-held sampled auscultatory sound data Sm I] is then
segmented into
separate heart cycles 82, b responsive to locations of the R-peaks 80' of the
associated
electrographic data 74 of the associated electrographic signal 37, wherein a
particular heart
cycle 82, b is assumed to start at one R-peak 80' and end at the next R-peak
80'. Beginning
with step (9008), for each heart cycle 82, b, with the heart cycle 82, b
starting at time 0, the
heart cycle duration ATR_R ms in milliseconds is given in step (9010) as the
relative time of the
next R-peak 80'. Then, in step (9012), the Weissler point DT., is then
calculated from
equation (14) hereinabove, and an associated midpoint TAHD is given as the
midpoint in time
between the start of the heart cycle 82, b and the Weissler point DT.s, i.e.
TMID = DT., /2.
Then, in step (9014), the start and end of each of the Si, S2, S3 and S4
intervals are defined for
purposes of determining associated swing values as follows:
TSl_START =0; TS1_END = TS2_START = TMID, TS2_END = TS3_START = DTms, TS3_END
=TS4_START =
0.8 * ATR-rt_ms; and TS4_END = ATR-R_ms=
Then, in step (9016), the associated swing values for each of SlswiNG,
S2swiNG, S3swiNG,
S4swiNG, i.e. SXswiNG for X=1, 2, 3 and 4 respectively, are given by the
absolute value of
difference between the maximum and minimum values within the associate time
intervals. Then,
in step (9018), the following swing ratios are calculated and saved for the
associated heart cycle
82, b: S4swiNG/S2swiNG, S4swiNG/S3swiNG, S3SWINGIS1 SWING,
S3SWINGIS2SWING, and
S2swiNG/Slswm1G, after which, in step (9020), one or more of the supplemental
swing measures
listed in FIG. 91 may also be calculated and saved in step (9100) for the
associated heart cycle
82, b. Then, from step (9022), the process of steps (9008) through (9020) is
repeated for all
heart cycle 82, b. From step (9022), after processing all of the heart cycles
82, b, in step
(9026), and optionally step (9026), the median values of the swing ratios and
supplemental
swing measures, the latter per step (9200) of FIG. 92, respectively, are
calculated, and in step
(9028), these median values are return to the point of invocation.
Referring to FIG. 93, the CAD detection process 9300 commences in step (9302)
for a
given test-subject 22, p to be evaluated, with the identification, in step
(9304), of various
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disease-relevant parameters of the test-subject 22, p, including, inter alia,
the age and sex (male
or female). Then, from step (9306), if the test-subject 22, p exhibits an S4
sound 104
responsive to the S4 detection process 8700, then, in step (9308), an S465
parameter is set to a
value of 1 for that test-subject 22, p to indicate the present of an S4 sound
104. Otherwise from
step (9306), if, from step (9310), the age of the test-subject 22, p is
greater than 65, then, in
step (9312), the S465 parameter is set to a value of -1 for that test-subject
22, p to indicate that
there is no S4 sound 104, and there also may be no coronary artery disease
(CAD). Otherwise,
from step (9310), in step (9314), the S465 parameter is set to a value of 0
for that test-subject
22, p to indicate that there is no S4 sound 104. Then, from steps (9308),
(9312), or (9314), if, in
to step (9316), the test-subject 22, p is male, then, in step (9318), an
IsMale parameter is set to
TRUE for that test-subject 22, p, otherwise, in step (9320), the IsMale
parameter is set to
FALSE. Then, in step (9322), breath-held sampled auscultatory sound data SmIl
containing
a plurality of heart cycles 82, b for the test-subject 22, p is received for
one or more
auscultatory sound sensors 12 m, and in step (9324), the plurality of swing
measures are
determined therefrom by above-described executive swing-measure determination
process,
8900, after which, in step (9326), the associated Support Vector Machine (SVM)
106 is
evaluated to determine whether or not the test-subject 22, p is likely to
exhibit coronary artery
disease (CAD) using inputs of S4 Present, Age, S465, IsMale, the swing
measures, and possibly
one or more of the supplemental swing measures, for each of the heart cycles
82, b, each of the
frequency ranges f, each of the auscultatory sound sensors 12 m, of the
associated test-
subject 22, p,
Referring to FIGS. 94-96, in accordance with a second aspect, a CAD detection
process
9600 utilizes a Support Vector Machine (SVM) 108 responsive to a set of
features F WI that
are determined -- for a particular test subject 22, PEVAL to be evaluated --
by an associated
CAD-detection feature-determination process 9500, the latter of which is also
used by an
associated CAD-detection training process 9400 to compile a set of features
F[p] associated
with a training set of test subjects 22, {p}rRAmiNG -- for whom a coronary-
artery-disease status
is also known -- to train the Support Vector Machine (SVM) 108.
More particularly, referring to FIG. 94, the CAD-detection training process
9400
commences with step (9402), for each of a plurality of test subjects 22, p,
with the
determination from step (9404), by the CAD-detection feature-determination
process 9500, of
a set of features F[p] to be used, along with associated coronary artery
disease (CAD) status, to
train the associated Support Vector Machine (SVM) 108.
Referring to FIG. 95, in step (9502) of the CAD-detection feature-
determination
process 9500, breath-held sampled auscultatory sound data SmI] containing a
plurality of
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heart cycles 82, b for the test-subject 22, p is received, and, in step
(9504), filtered using a
bandpass Butterworth filter for each of the following five frequency
intervals: 1) 1 Hz to 10 Hz;
2) 11 Hz to 30 Hz; 3) 3-31 Hz to 80 HZ; 4) 81Hz to 120 Hz; and 5) 121 Hz to 1
KHz. Then, in
step (9506), for each frequency interval, the resulting filtered breath-held
sampled
auscultatory sound data Sm[] is, in step (9508), segmented into separate heart
cycles 82, b
responsive to locations of the R-peaks 80' of the associated electrographic
data 74 of the
associated electrographic signal 37, and in step (9510), the separate heart
cycles 82, b are
temporally aligned with respect to the start of Si, and, in step (9512),
temporally scaled similar
to steps (3724) and (3728) of the above-described auscultatory sound signal
preprocessing
and screening process 3700, but with application to the entire heart cycles
82, b, and not just
the diastolic portion thereof Then, in step (9514), data from the separate
heart cycles 82, b is
averaged to form a single, waveform of average filtered breath-held sampled
auscultatory
sound data Sm[] for a single heart cycle 82, b for the associated frequency
interval. Then, from
step (9516), the process of steps (9506) through (9516) is repeated for each
frequency interval.
From step (9516), after the average filtered breath-held sampled auscultatory
sound data Sm1]
has been generated for each of the frequency intervals, then, in step (9518),
the integral, median
and mean of both the standard energy waveform and the Shannon energy waveform
are
calculated for each of the frequency intervals and within each of the seven
time intervals
identified hereinabove in and in conjunction with FIG. 77, -- i.e. the Si
interval, the systolic
interval, the S2 interval, the pre-S3 interval, the S3 interval, the diastasis
interval, and the S4
interval, -- so as to generate the associated set of features F[p] that are
used as inputs to the
Support Vector Machine (SVM) 108, wherein with the standard energy is defined
by:
1 NH
=¨AT lx(k-For
(38)
- k-O
and the Shannon energy is defined by:
1 Nw
E = ¨ (x (k + i))2 ln(x (k + i))2 (39)
AT k=0
for example, using a window length Nw of 400 samples, (i.e. 10 milliseconds),
after which, in
step (9520), the resulting set of features F[p] are returned to the point of
invocation.
Returning to FIG. 94, following the determination from step (9404) of the set
of
features F[p] for the selected test subject 22, p to be used to train the
Support Vector
Machine (SVM) 108, from step (9406), if the test subject 22, p is known a
priori to exhibit
coronary artery disease (CAD), then the value of an associated binary
CAD_Status[p] flag is set
to TRUE in step (9408). Otherwise, from step (9406), if the test subject 22, p
is known a priori
to not exhibit coronary artery disease (CAD), then the value of the associated
binary
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CAD_Status [pi flag is set to FALSE in step (9410). Then, from either of steps
(9410) or
(9408), in step (9412), if all of the test subjects 22, p have not been
processed, then steps
(9402) through (9412) of the CAD-detection training process 9400 are repeated
for the next
test subject 22, p. From step (9412), after the sets of features FP[pl and
associated
CAD_Status[p] are determined for each of the test subjects 22, p, then, in
step (9414), the
Support Vector Machine (SVM) 108 is trained responsive thereto to detect
coronary artery
disease (CAD).
Referring to FIG. 95, following the training of the Support Vector Machine
(SVM) 108
by the CAD-detection training process 9400, the Support Vector Machine (SVM)
108 may
then be used to determine whether or not an arbitrary test subject 22, pEyAL
exhibits coronary
artery disease (CAD). More particularly, the CAD detection process 9600
commences in step
(9602) with the input of heart data associated with the test subject 22,
pENTAL to be evaluated.
Then, in step (9604), the set of features FP[pENTAL] is determined by the CAD-
detection feature-
determination process 9500, supra, for the test subject 22, pEVAL to be
evaluated, after which,
in step (9606), the Support Vector Machine (SVM) 108 -- as trained by the CAD-
detection
training process 9400 - is then used to determine whether or not the test
subject 22, pENTAL to
be evaluated exhibits coronary artery disease (CAD).
While specific embodiments have been described in detail in the foregoing
detailed
description and illustrated in the accompanying drawings, those with ordinary
skill in the art will
appreciate that various modifications and alternatives to those details could
be developed in light
of the overall teachings of the disclosure. It should be understood, that any
reference herein to
the term "or- is intended to mean an -inclusive or- or what is also known as a
"logical OR-,
wherein when used as a logic statement, the expression -A or B" is true if
either A or B is true,
or if both A and B are true, and when used as a list of elements, the
expression "A, B or C" is
intended to include all combinations of the elements recited in the
expression, for example, any
of the elements selected from the group consisting of A, B, C, (A, B), (A, C),
(B, C), and (A, B,
C); and so on if additional elements are listed. Furthermore, it should also
be understood that the
indefinite articles "a" or "an", and the corresponding associated definite
articles "the' or "said",
are each intended to mean one or more unless otherwise stated, implied, or
physically
impossible Yet further, it should be understood that the expressions "at least
one of A and B,
etc.", -at least one of A or B, etc.", -selected from A and B, etc." and -
selected from A or B,
etc." are each intended to mean either any recited element individually or any
combination of
two or more elements, for example, any of the elements from the group
consisting of "A-, "B-,
and "A AND B together", etc. Yet further, it should be understood that the
expressions "one of A
and B, etc." and "one of A or B, etc." are each intended to mean any of the
recited elements
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individually alone, for example, either A alone or B alone, etc., but not A
AND B together.
Furthermore, it should also be understood that unless indicated otherwise or
unless physically
impossible, that the above-described embodiments and aspects can be used in
combination with
one another and are not mutually exclusive. Accordingly, the particular
arrangements disclosed
are meant to be illustrative only and not limiting as to the scope of the
invention, which is to be
given the full breadth of the appended claim, and any and all equivalents
thereof
What is claimed is:
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