Note: Descriptions are shown in the official language in which they were submitted.
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IN THE UNITED STATES PATENT AND TRADEMARK OFFICE
PCT PATENT APPLICATION
TITLE:
METHOD AND APPARATUS FOR DETECTING SEIZURES INCLUDING AUDIO
CHARACTERIZATION
Inventor:
Michael R Girouard
Citizenship: US
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METHOD AND APPARATUS FOR DETECTING SEIZURES IINCLIJDING AUDIO
CHARACTERIZATION
CROSS REFERENCE TO RELATED APPLICATIONS
WWI This
application (Innis priority to U.S. Provisional Patent Application No,
61/875,429 filed September 9, 2013. U.S. Provisional Patent. Application No,
6015,236 filed
December 12, 2013, U.S. Provisional Patent Application No. 61/969,660 filed
March 24, 2014. U.S.
Provisional Patent Application No. 61/979,225 filed April 14, 2014, and U.S.
Provisional Patent
Application No. 62,001,302 filed. May 21., 2014, and is a continuation-in-part
of U.S. Patent
Application Serial No, 13/2.75,309 filed October 17, 2011, which claims
priority to US, Provisional
Patent Application Serial No. 61/393,747 filed October 13, 2010 and is a
continuation-in-part of U.S.
Patent Application Serial No. 13/542,596 filed July 7, 2012, which claims
priority to U.S. Provisional
Patent Application Serial No. 61/504,382 filed July 5.201.1.
BACKGROUND
10002I A
seizure may be characterized as abnormal or excessive synchronous activity in
the brain. At the beginning of a seizure, neurons in the brain may begin to
fire at a particular location.
As the SeiZIM progresses, this firing of neurons may spread across the brain,
and in sonic cases, many.
areas of the brain may become engulfed in this activity. Seizure activity iii
the brain may cause the
brain to send. electrical signals through the peripheral aervous system to
different muscles the
activation of which may initiate a redistribution of ions within muscle
fibers. In electromyography
(TAW), an electrode mtn,' be placed on or near the skin and configured to
measure changes in
electrical potential resulting from ion flow during this muscle activation.
100031 EMU
detection may be particularly amenable for use in apparatuses that may be
minimally intrusive, minimally interfere with daily activities and -which may
be comfortably used
while sleeping. Therefore, methods of monitoring the seizure activity of
patients, including methods
for monitoring in ambulatory or home settings, may benefit from the use of EMG
detection. For some
patients, a. seizure event may also be presented as an audible scream or
vocalization which may
typically occur at the start of a seizure. Like EMG detection, audio detection
of seizures may be
particularly amenable to methods of patient monitoring that may be minimally
intrusive, and
monitoring of seizure activity using one or more acoustic sensors individually
or M combination with
E.MG may be used. in improved methods of monitoring a patient for seizure
activity.
SUMMARY
100041 In some
embodiments, a method of monitoring a patient for seizures with motor
manifestations may comprise monitoring a patient using one or more EMG and
acoustic sensors and
(determining whether data collected using either sensor type exceeds a
threshold value. In some
embodiments, if a threshold value is met, a patient may be further monitored
for a subsequent period
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of time and an alarm protocol may be initiated if a corroborating event or
second threshold is reached
during that subsequent time period,
BRIEF DESCRIPTION OF THE DRAWINGS
10005j Fig. 1
illustrates one embodiment of a seizure detection system that includes one
or more acoustic sensors.
100061 Fig. 2
illustrates one embodiment of a method of monitoring a patient using data
collected or received from an acoustic sensor.
100071 Fig. 3
illustrates a further embodiment of a method. of monitoring a patient using
data collected or received from an acoustic sensor.
100081 Fig. 4
illustrates a method of monitoring a patient using data collected or received
from an acoustic sensor and that may be used together with EMG data.
10001 Fig. 5
illustrates another method of monitoring a patient using audio data. that
may be used together with EMIG data.
DETAILED DESCRIPTION
10001 01 The
apparatuses and methods described. herein may be used to detect seizures and
timely alert caregivers of seizure-related events and may further be used to
provide early indication
that a detected seizure event fluty pose certain risks of adverse effects
including SUDEP. The
apparatuses may include sensors attached to a patient or patient's clothing
and may be configured for
measurement of muscle electrical activity using electromyography (EMG).
Detection of seizures
using EMG electrodes andlar other sensors is further described.. thr example,
in Applicant's U.S.
Patent Application No. 13/275,309 and 13/542,596 and Applicant's U.S.
Provisional Patent
Application Nos. 61/875,429, 61,910,827, 61/915.236, 61/969,660, 61/979,225,
and 62/001,302 the
disclosures of each of which are herein fully incorporated by reference. As
described herein., an
acoustic sensor may further be used to monitor the state of a patient, and in
some embodiments, audio
data may be collected or received from an. acoustic sensor and/or stored along
with .EMG data. Audio
data may be used to enhance the accuracy of real time seizure detection and/or
used in review of
collected sensor data. For example, audio data may be collected, analyzed in
real-time, and used in
making a decision about whether to alert a cainglyer that a patient may be
experiencing a seizure. In
some embodiments, audio data may be used to corroborate the detection of
seizure activity based on
one or more portions of EMG data. including EMG data collected during one or
more early or pre-
seizure time periods, and may, in combination with the EN.ICi data, be used to
initiate an emergency or
other alarm response. Collected audio data may also be analyzed at times after
a period of monitoring
and may be used toverifv Whether a seizure or seizure Mated event has
occurred.
1000111 In some
embodiments, monitoring a patient using collected or received audio data
may be either semi or fully automated. For example, a monitoring system may be
configured to
operate without the need for interpretation by a remote caregiver using a
processor configured to
analyze the data for features characteristic of seizure activity, in some
embodiments, a processor may
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he configured to identify repetitive patterns included in audio data that meet
one or more criteria that
.may be indicative of a seizure and weigh the presence of those patterns in a
method that may be used
to trigger an alarm or initiate another system response. And., those methods
may be automated without
need for caregiver input or interpretation. Alternatively, audio data may be
trans.mitted to a remote
caregiver for interpretation. Particularly, data suspected of being related to
seizure: activity may be
sent to a caregiver for review after initial identification or screening using
an automated program. For
example, a processor may be configured to identify patterns associated with
seizure activity and if
those patterns are found present, audio data may be transmitted to a caregiver
for further interpretation
and/or verification of seizure activity. Therefore, a processor may be
configured to directly trigger an.
alarm using one or more ale,oridmis that include audio data or may be
configured to fitter sounds from
other sound features identifying those most likely to indicate the presence of
a seizure.
10M121 hi some
embodiments, audio data may be processed in order to calculate one or
more input values for use in a seizure detection algorithm. A detection
algorithm that incorporates
audio data may operate individually or in combination with other data to
detect a seizure. For
example, in some embodiments. audio data may be input into a monitoring
routine that also includes
inputs derived front one or more .EMG and/or other sensors. In some of those
embodiments, an audio
detection routine may focus on one type of seizure: or a particular
manifestation of one or more seizure
types. For exampleõ a patient experiencing a seizure may sometimes produce
characteristic sounds
indicative of respirators stress, but for other seizures, the patient may fail
to produce that particular
sound pattern.. An audio detection routine may be configured to be selective
ibr one or more particular
manifestations of seizure activity and when identified confidence in detection
may be high, However,
in some embodiments, it may be -beneficial to combine audio detection with
other sensor data
particularly including EMG which may be made highly responsive to generalized
seizure activity.
And, iii some embodiments, audio demotion may be combined with EMG not only to
improve
detection efficiency but also to help classify identified seizures. in some
embodiments, :more than one
audio demotion routine may be run together in a. method of analyzing data for
various audio signatures
that may be present tbr different seizure manifestations. For eNample, in some
embodiments, one
audio detection routinc, may examine audio data for the presence of a high
amplitude signal that may
indicate a. scream or examine audio data for a high amplitude signal followed
by a sustained portion of
lower audio amplitude and a second audio detection routine may examine audio
data for one or more
patterns and determine if the patterns show periodicity indicative of one or
more parts of a seizure.
Those routines may, in sonic embodiments, be patient specific, and tailored to
detect sounds particular
.for a given patient or patient demographic. And, in some embodiments, voice
recognition software
may be used to identify that a given sound was derived from a certain patient.
1000131 Audio
data may. in some embodiments. be collected, or received during one or
.more time periods and characteristics of the data calculated over time. For
example, a characteristic
derived front audio data may be a metric related to the strength or power of a
sound wave from. which
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the data was derived such as a signal amplitude or amplitude as compared to a
reference level and a
value for the characteristic may be expressed, for example, in decibels or
another relative -unit
expressing amplitude, strength, or power of a sound wave. A characteristic or
audio data may be
tracked and trends in the data may be analyzed for seizure characteristics.
For example, a
characteristic such as signal amplitude may he considered over time and the
presence of one or fume
data Patterns or key Points in the signal (such as local maximum values or
local maximum value
meeting some threshold amplitude may be determined). A local maximum value may
be related to a
particular physical activity executed by the patient (such as gasping of air)
and may repeat. For
example, each time the patient: executes the activity a local maximum value or
local maximum value
meeting some threshold amplitude may be present.. By tracking the position of
local maximum values
or other repeating pattern or value the underlying activity executed by the
patient may then be
monitored. For example, the periodicity andlor duration of intervals of time
of or between repetitive
patterns of audio data may be determined and compared to those typical for a
patient experiencing a
seizure. As used herein, the term "periodicity" refers to how regular a
certain pattern may manifest or
repeat over time. In sonic embodiments, one or more characteristics or audio
data may be detennined
and used to identify one or more repetitive data patterns. Characteristics of
audio data may, by way of
nonlimitiug example, include audio signal intensity or amplitude, amplitude at
a. given frequency (or
over a certain frequency range), rate of change of amplitude, spectral slope;
other data, or
combinations of audio characteristics thereof in sonic embodiments, data from
a. collected or received
signal may be compared to one or More model patents of data associated with an
activity that may
typically .repeat for a patient: experiencing a seizure. For example, using
pattern recognition software
similarity of data to a model pattern may be determined (such as by using
regression analysis), and a
certainty value for whether a given portion of data match the pattern may be
determined. A certainty
that a detected pattern corresponds to an activity executed by a patient
during a. seizure may be
determined and to increase the confidence that data may properly be identified
as related to a seizure
trends in the pattern over time may be determined. For example, when a patient
is under respiratory
Stress they nut tend to gasp repetitively over time, but as the patient tires
sound produced during
gasping may weaken or shift in frequency. When examining collected or received
data expected to
-match a -patient: activity (such as a gasp) changes andlor shifts in the data
may be compared to those
typical for a patient experiencing a seizure (during, normal or abnormal
seizure progression) and if
those changes andfor shifts are within expected bounds certainty of seizure
detection may be
improved.
1000141 In some
embodiments, to identify a repeating pattern in collected or received
audio data one or more algorithms may be executed to compare data to a model
set of data derived or
recorded from one or more actions executed by a patient during a seizure and.
a certainty value may be
assigned to an identified portion of data such as using one or more data
regression algorithms. For
exa-mple, collected data and model data may be overlaid (varying the relative
position of a set of
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clinical data and model data), and in some embodiments, a point-to-point
analysis of deviations ({Or
each varying position) may be executed and when overlaid as appropriate to
minimize the deviations a
similarity x.'alue between the clinical patient data and model data may be
determined. If the overall
deviation between points is suitable a pattern may be deemed to be detected.
To further relate the
pattern to seizure activity, a periodicity Value of a plurality of detected
patterns may then be
determined. In some embodiments, data may also be processed by one or more
algorithms to identify.
that the sound is related to a patient. An algorithm to identify that a sound
is related to a patient may,
for example, include or be based on any of various voice recognition
algorithms or programs.
po0151 In some
embodiments, audio data may be filtered and/or corrected to account for
ambient noises or a level of ambient noise, and in some embodiments, spatial
filtering, of an audio
signal may be used to isolate sounds originating from different locations
within or near a region of
monitoring. In sonic embodiments, audio data may be classified based one or
more events that may
produce a. certain sound or sound. component. For example. audio data may be
classified as being
characteristic of any number of events including by way of nonlimiting example
occurrence of a
seizure, human speech, shutting of doors, barking of a dog, walking, ringing
telephone, other events,
and combinations thereof Some events may be deemed background noise that may
not indicate the
presence of a seizure. That is. non-seizure related sources of noise may be
characterized. .in some
embodiments, events that may be indirectly produced by a patient during a
seizure may be
characterized. For example, during a clonic-portion of a seizure, a patient
may move back and forth
causing oscillation of nearby objects, such as furniture, which may produce an
audible sound. And, in
some embodiments, an object such as an item of furniture may be putposefully
modified to produce a
characteristic sound when moved in a rhythmic manner, For example, a bell or
other sound device
may be associated with an item of furniture that produces a characteristic
sound in response to nearby.
movement. Preferably, that bell may produce an oscillation that is accurately
captured by an acoustic
transducer the oscillation being different than other sounds. For example, a
sound making device may
oscillate at a frequency that is readily passed by an inverse notch or
combination of high pass and low
pass filters. hi some embodiments, to facilitate classification of audio data,
sounds may be
characterized in terms of intensity, spectral shape or other cliaracteristies
and stored in a database for
comparison to data collected during monitoring. Collected data and/or
spatially filtered data may be
fit to data derived from one or more known sounds and a probability that a
sound or component of a
total sound may be provided from a seizure or discounted as associated with a
non-seizure event)
may then be calculated and used in a seizure detection algorithm.
1000161 In some
embodiments, audio data may be collected using one or more monitoring
routines that may run intermittently or that may be configured to trigger
certain responses only if
activated by being preceded within a time period by other events. For example,
audio data may, in
some embodiments, be collected. but may only initiate an alarm response if the
audio data is
temporally correlated with the detection of EN1.0 data associated with. a
seizure related. event, For
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example, some routines for electromyography may examine whether a patient may
be experiencing
'Weak motor 131anifestailatis typically present prior to a seizure. And, if
those routines produce a
response, it may be deemed that the patient is at risk of having a seizure..
In some embodiments, weak
detections may terminate passively without interrupting the patient or produce
an active response if,
for example, the weak evenis. fail to terminate or if the detection is
corroborated by another event. In
some embodiments, corroboration of initial motor manifestations of a seizure,
including
manifestations detected prior to or without a clothe phase portion of a
seizure. may be made based on
one or .more detected audio patterns. That is. in some embodiments, an audio
detection routine may be
executed or activated to provide a given response only if preceded by a
detection of prior EMG data.
For example, if weak motor manifestations are detected with E.MG, an audio
detection routine may
become active such that the routine may issue an alarm if the audio data
indicates the presence of
seizure activity and corroborates the .FMG. data, Because those weak motor
manifestations may only.
.be present intermittently ¨ whether a seizure actually .manikets or not, the
probability of inadvertent
.false-positive initiation of an alarm based on collected audio data may be
minimized.
[000171 A
variety of systems may be suitably used, ibr collecting EMG, audio, and other
patient-related data, prioritizing data for storage, organizing such data for
system optimization, and/or
initiating an alarm in response to a suspected seizure. Figure 1 illustrates
an exemplary embodiment
of such a system. In the embodiment of Figure 1, a seizure detection system 10
may include a video
camera 9, a detection unit 12, an acoustic sensor 13, a base station 14, and
an alert transceiver 16. The
detection unit may comprise one or more EMG electrodes capable of detectirta,
electrical signals from
muscles at or near the skin surface of a patient, and delivering those
electrical EMG signals to a
.processor for processing. The base station may comprise a. computer capable
of receiving and
processing .EMG signals .from the detection unit and/or acoustic data from an
acoustic sensor,
determining from the .processed EMG and/or acoustic signals whether a seizure
may have occurred,
and sending an alert to a caregiver. An alert transceiver may be carried by,
or placed near, a caregiver
to receive and relay alerts transmitted by the base station. Other components
that may be included in
the system 10, including for exam.ple, an alert transceiver 16, wireless
device 17, 18, storage database
19, and one or more environmental transceivers (not shown) are described in
greater detail in
Applicant's U.S. Patent Application Nos, 13/275,309 and 13/542,596,
1000181 As shown
in Figure 1 one or more acoustic sensors 1.3 may be included in a
detection system 10. Acoustic sensors may, for example, be placed at one or
more locations within or
near a monitoring area. .An acoustic sensor may, in some embodiments, be
attached to a patient or
patients clothing. Therefore, an acoustic sensor may be attached and may move
along with a patient
or may remain stationary as a patient moves. In Figure 1, aCOUSUC, sensor 13
is shown -to be a separate
twat from other elements. For example. a deteetion unit 12 may be attached. to
one arm of a patient and
an acoustic sensor 13 may be worn on the same or other arm, However. an
acoustic sensor may also
he integrated into one or more other devices. For example, an acoustic sensor
may be illiegtated into
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any of video camera 9, detection unit 12, base station 14, or integrated in
Some other device or
e le Merit
1000191 Figure 2
illustrates an exemplary method 20 of analyzing an audio signal for
seizure characteristics. in a step 22, an audio signal may be collected using
one or more acoustic
sensors or data may be imported into a processor for aualysis. An acoustic
sensor or microphone may,.
.for example, include an acoustic-to-electric transducer suitable for
convening a sound wave into an
electrical signal. A transducer may, in some embodiments. operate without
significant signal
distortion over a desired frequency range which may, for e.Nample., include
the frequency range of
human speech and/or include other frequencies such as may be useful to
spectrally characterize arty of
various sources of environmental noise or sound producing devices including
those that may be
specifically associated with one or more units of .furniture or objects in a
monitoring locale. As used
herein, spectral characterization of acoustic data refers to description of
signal intensity over one or
more frequencies. In the step 24, a collected or received audio signal may be
processed to determine
the value alone or more characteristics of the audio data. For example, in the
step 24, signal may be
processed or conditioned such as to remove background noise and/or to isolate
a desired frequency
'hand or distribution of .frequency bands. In some embodiments, signal may be
processed through an
analog-to-digital converter suitable for processing of signals that may be as
high as about 5 KHz to
about 10 .K.HZ, In some embodiments, one or more high and/or low pass filters
may also be used to
condition a collected audio signal.
1000201
Processing may, in some embodiments, further include comparison of signal to
audio data previously acquired during one or more reference periods. For
example, a reference period
.may be oollected, and baseline audio characteristics of the reference period
such as a baseline level of
an audio characteristic and/or noise fluctuations in an audio characteristic
may be established. Audio
signal collected may, in some embodiments, be processed by scaling a
characteristic of audio data in
terms of a ratio to a baseline value or scaling in terms of a number of
standard deviations above a
characteristics baseline noise level. For example, amplitude of audio data or
amplitude over one or
more frequency bands may be a. characteristic that: may be compares to
baseline amplitude levels
and/or otherwise scaled by comparison to a baseline levels of amplitude.
1000211
Processing of data in the step 24 may be used to determine the .value of one
or
more characteristics of audio data. For example, in some embodiments,
processing of data may be
used to assess how a characteristics of audio data, such as its amplitude,
tracks over time. For
example, in some embodiments, processed audio data may be amplitude data
associated with a
desired portion of monitored frequencies, and in some embodiments, amplitude
data may include all
or a sawed portion of collected frequencies.
1000221 Upon
processing of data to determine characteristic values for the data and how
the values tracks over time an algorithm may further examine whether
characteristic value change
over time in a manner expected. for seizure activity. For example. in the step
26, in some
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embodiments, processed data may be analyzed to identify distinct points among
the determined values
for the characteristic. and examine whether the distinct points meet one or
more periodicity
requirements associated with seizure .activity. For example, a distinct point
may be identified if the
point meets a threshold amplitude value, and the timing or periodicity between
-those points may then
be examined. That is, step 26 may include compaeing- data values for a
characteristic tracked over
time (as describe in step 24), identiling distinctive or critical points based
on meeting a threshold
Criterion and determining if the timing between distinct or critical points
(net- times meets a
periodicity requirement
[900231 In some
embodiments, a plurality of distinct points may be assessed and
periodicity values ibr times between the points may be determined, However,
some trends in an audio
signal may not repeat. For example, in some seizures, an initial or high
intensity scream (as further
described below) may be present and, in sonic embodiments, an initial high
intensity scream
(sometimes followed by a sustained. period of lesser amplitude signals) may be
identified by analyzing
processed audio signal And, while in some embodiments. audio signal may be
input together with
other sensor data (preferably F.,tai data) to detect a seizure, in other
embodiments, one or more
characteristics of audio signal may be used to directly trigger an. alarm. For
example, if an audio
signal is collected or received (step 22) and if ampliwde is tracked over time
(step 24) and in analysis
of amplitude trends (step 2.6) signatures of a high intensity scream followed
by a delay period and
-then a repeating series of distinct points or patterns indicative of a
plurality of gasps is detected
confidence in seizure detection may be high.
1000241 In some
embodiments, processing and analysis of audio signal may include
-running one or more pattern recognition programs, to identify within audio
data if a certain portion of
the data matches a pattern. For Maniple, in some embodiments, a distinctive or
critical point (as
described above) may be a part of 3 pattern including, for example, a. pattern
modeled after an activity
commonly executed during a seizure. In some embodiments, pattern recognition
may include
smoothing a set of data, identification of one or more extreme values in a
data set, and applying one or
more procedures including overlay and regression analysis. For example, a
program may identify a
local maximum value in an audio data set and attempt to fit data around the
local maximum to one or
more model functions associated with a certain sound. For example., a MOM
sound may represent or
he derived from a recording of a patient gasping for air and a given set of
data may be compared to
the model sound by overlaying and fitting collected data using regression
analysis and determining if
the sound meets a threshold level of similarity to the model sound. For
example, an algorithm may
detennine if a certain portion of data matches a pattern of a gasp or matches
the pattern of a gasp at
some probability.
1000251 During a
seizure some patients may shout, grunt, or gasp and the overall
amplitude or intensity of a resulting acoustic signal may be high. The
presence of a spike or sustained
spike in acoustic sensor amplitude may therefore tend to correlate with a
seizure state. However, other
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events may also tend to produce high amplitude audio signals. Therefore, in
preferred embodiments,
.processed signal may be analyzed in order to discriminate acoustic data from
non-seizure sources, in
various embodiments described herein. discrimination of acoustic data from non-
seizure events may
be achieved in various ways.
1000261 For
example. when some patients experience a seizure the patient may force a
large amount of air through their throat and an audible signal may tend to be
produced. Some patients
may tend to -take in and expel. air from the lungs in a repetitive manner, and
a resultant sound pattern,
sometimes characterized as a grunt or gasp, may be repeated in time with a
degree of regularity. Some
embodiments herein may analyze a collected audio signal for the presence of a.
sound pattern that
resembles a seizure grunt or gasp. Furthermore, some embodiments may determine
if the sound
pattern is repeated, and a repeating sound pattern may be used to detect the
presence of a seizure.
Pattenlady,. .the .periodicity of a sound pattern of a seizure may be more
regular and/or may., for some
seizures, include a lower frequency component than sonic other sounds
including for example normal
human speech. For example, normal human speech may tend to have more variation
than sounds
produced during a seizure. Moreover, the regularity of sounds produced in a
seizure may be more
random in human speech and generally not vary in the same manner as someone
who may, far
example, be struggling to take in and expel air repetitively as in certain
parts of a seizure.
14.100271 The
repetition rate of individual members of a repeating sound pattern for a
patient experiencing, a seizure may be characterized, and for some patients
the number of pattern
members present over time may be about 0.5 to about 5 member patterns per
second. For example, for
some .patients at least about three members of a repeating sound pattern for
every second may be
.present at the start of one part of a seizure with the number typically
dropping during the seizures
progression. That number may drop steadily through a Minns progression or
terminate abruptly.
That progression may be characterized over time and communicated to a
caregiver and may be
compared to models of progression including those for normal and abnormal
seizure progression or
recovery, in some embodiments, the periodicity of a repeating sound pattern
may be determined for
an individual patient or estimated for a patient based on one or more patient
characteristics (e.g.,
patient age, gender, height, and/or weight), and in sonic embodiments, an
expected periodicity of a
seizure sound pattern may be estimated prior to patient monitoring.
1000281 In some
embodiments, sound. may be collected and a pattern re.counition
algorithm may probe resulting acoustic, data for one or more distinguishing
patterns. For example.
sound may be collected and processed to identify portions of audio data
associated with a repetitive
seizure sound, A distinguishing pattern may be identified based on the
presence of a certain data
feature or combination of data features. For example. the presenee of a
threshold local maximum
amplitude, threshold local .maximum amplitude followed 1)y a sustained period
or decreasing acoustic
amplitude, or threshold local maximum with surrounding portions similar to one
or more model
functions may be used to identifY a pattern. To identify a. pattern. audio
data. may be binned and
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integrated over time units tor bins) to improve signal to noise. The data may
be binned within periods
of time as may be appropriate to track relevant: changes through a period of
time such as during
inhalation and/or exhalation during a seizure grunt or gasp. For example, in
some seizures, audio data
from a grunt may change more slowly as one is taking in air and more rapidly
as the diaphragm forces
air Out of the lungs. Some patients may tend. to make a recognizable sound
near times following when
air has been mostly pushed out of the lungs. For example, the patient may gasp
to try and catch their
breath. And, to reliably capture sounds produced during contraction and/or
expansion of the lungs
data may, for example, be binned and integrated over periods of up to about 50
milliseconds. A
.repeating sound pattern may, in some embodiments, be broken up into various
parts and individual
parts of the sound pattern may be identified. For example, during inhalation
and exhalation different
sounds may be made and by examining audio data for a characteristic pattern
associated with
inhalation followed by exhalation abnormal sounds associated with a seizure
may be identified. For
example, because normal breathing may show a more symmetric profile of
inhalation and exhalation
than some seizures, breaking up a sound into a lust pattern associated with
inhalation and a second
pattern associated with exhalation :may he used in algorithms for detecting
the presence of a seizure.
That is, the relative time in which a patient is deemed inhaling and exhaling
may be identified and a
ratio of inhalation time to exhalation time may be determined.. A ratio that
is significantly different
than about 1:1 Ouch as outside of a range extending from about 0.8:1 to about
1.2:1) may be used to
characterize respiratory stress and possible seizure activity. Particularly,
in some embodiments, a
detected sound may be examined for characteristics of a seizure grunt Or gasp,
which may include
breaking .up the data and. looking for parts of data typical of inhalation and
typical of exhalation and
characterizing whether the duration of the pans are more or less symmetric in
duration. That is, for
struggled breathing, temporal asymmetry with one part lasting longer than the
other may be identified.
1000291 An
algorithm may further determine whether an identified data pattern maintains
an expected periodicity. For example, while portions of a grunt may show
asymmetry between
inhalation and exhalation parts the overall pattern of inhalation and
exhalation may be characterized
as having higher regularity than other sounds including speech. For example.,
if a. pattern is present
and repeats over time with a regularity of about once every 0,2 to about 2
seconds,, and the pattern is
detected a number of times (such as at least 4 to about tO times) or over a
certain period initiation of a
seizure alarm may be encouraged. Any of various points within a detected,
pattern may be used to
identify timing at which a detected pattern occurs and may further be used to
assess the periodicity of
the pattern. For example, the start, middle or ending time of a detected
pattern may be used. Most
patterns described herein may include a local maximum amplitude value that
meets some threshold
and the time of that value may be conveniently used to ident4 the position in
time of a detected
pattern.
1000301 In some
embodiments, changes in .periodicity over time may be tracked (even
after an alarm may be initiated), and for example. an. algorithm may look for
signs of abnormal
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recovery from a seiztom The periodicity of a repeated sound pattern may
further, in some
embodiments, be compared to the periodicity of EMG data bursts. For example,
both EMG data
bursts and periods of respiratory stress may be related to the presence of
uncoordinated signals sent
from different parts of the brain and for some patients the phase and/or
periodicity of bursts and the
phase and/or periodicity of audio data produced during periods of respinnory
stress may be related
and/or tracked together including to identify when a patient may be showing
abnormal signs of
seizure progression and/or recovery.
10003II: In some
embodiments, audio data may possess high amplitude (often associated
with characteristic frequency changes) during times of a grunt or gasp right
after exhalation begins.
More generally, any point or points in a pattern including for example points
identified as meeting a
threshold requirement or condition or other distinct characteristic may be
identified and used in a
calculation of periodicity. For some patients, during some. portions of a
seizure a characteristic grunt
may be high in amplitude and the patient may repeat a similar sound, but
muscle fatigue may dampen
the overall amplitude of the sound pattern. That is, a repetitive pattern may
be identified some number
of times but later repeats may be characterized as having lowered amplitude.
Likewise, for some
patients one or more periodicity values may drift over time. Therefore, in
some embodiments,
detection of a characteristic pattern in audio data accompanied by a dampening
of overall signal
amplitude and/or trends in .periodicity may be used in a seizure detection
algorithm.
1.000321 In some
embodiments, audio data may be collected and analyzed over a plurality
of time intervals. For example, audio data may be analyzed over time intervals
as appropriate to
capture amplitude and/or frequency changes that may occur during the course of
a seizure. For
example, in sonic embodiments, audio data may be divided into intervals of
about. 0.01 to about 0.1
seconds. During any given interval one or more characteristic value of audio
or processed audio data
may be calculated and the characteristic value(s) may be stored. An algorithm
may analyze
characteristic values from successive collection intervals or analyze smoothed
data over a period of
time and look for one or more characteristic patterns. Upon identification of
two or more repeating
pattern members, an algorithm may. determine whether the pattern meets one or
more periodicity
_requirements for a seizure. For example, a pattern may be identified by
meeting a threshold condition
such as the presence of a threshold acoustic amplitude value or threshold
acoustic amplitude that is a
local maximum, and a method may determine a time interval between detected
patterns. For example,
a time interval between adjacent detections of two threshold amplitude values
may be determined. If
the dine period between the threshold values is characteristic of a seizure
state an alarm may be sent
or an alarm may be sent if corroborated by other data.
1000331 A method
30 of monitoring a patient for seizure characteristics based on the
periodicity of one or more: distinctive points or characteristic patterns
identified from an acoustic
signal is illustrated in Figure 3, In a step 32, signal or suitably processed
signal (e.g., filtered or
background corrected signal) may be collected or received for a time interval
and one or more data
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values rimy be calculated from the collected acoustic or audio signal. Data
values calculated for an
interval may include, by way of nonlimiting example, amplitude data and, in
sonic embodiments, the
amplitude data may be associated with one or more spectral frequencies. For
example, a patient
izaspina for air may tend to produce sounds in one or more frequency bands and
in some routines for
analysis of audio data amplitude data may be isolated based on recorded
frequencies for a patient or
certain patient demographic.
1000341 In a
step 34, calculated data -value(s) may be stored, and in a step 36 stored data
values ineludirw, data from other nearby intervals may be analyzed to idea*
data that meet one or
.more criteria. As described above, in some embodiments, one or more pattern
recognition programs
may be executed on a set of data over dine (e.g., data associated with a
number of adjacent time
intervals). In some embodiments, if an amplitude of an audio signal in a time
interval exceeds a
certain threshold or if an audio signal is greater in amplitude than other
amplitudes in. nearby time
intervals (e.g., if the audio signal qualifies as a threshold local maximum
value) the acoustic data may
satisfy a threshold amplitude criterion. The point may be deemed distinctive
and used in further
calculations.. Other distinct or threshold points may also be identified. For
example, in sonic
embodiments, a local minimum in amplitude or an inflection point in amplitude
derivative data may
be identified. More generally, in some embodiments, a distinctive or
identified point may be any point
in a detected pattern such as the start, middle, or end of a detected pattern
that may reliably time
stamp when the pattern was detected.
1000351 For some
patients. acoustic data may be characterized by 'changes in spectral
characteristics. For example, during one portion of a seizure period, such as
during initial portions of a
grunt, the average frequency of data may be different than the average
frequency in other seizure
periods such as later portions of the grunt. That is, the dominant frequencies
of sounds produced by a
patient during a seizure may change, and in some embodiments, a. deteetion
algorithm may identify if
the frequency distribution of acoustic data changes in a defined manner to
meet a criterion. For
example, a. grunt or gasp may extend over multiple time intervals and in each
interval an average or
median frequency of signal data may be determined. The average frequency may
change over the time
period of a grunt and for some patients may, for example, move to higher
frequencies and then to
lower frequencies over time. Therefore, a data value calculated in a step :32
may be the average or
median frequency value of signal collected during art interval. The data may
be stored in a step 34 and
compared .to other frequency values in nearby intervals in a step 36. For
example, if data in an
interval is at a point where the average frequency transitions between
increasing to decreasing or
.transitions from decreasing to increasing the time interval may be marked. in
some embodiments. a
method may determine whether a. threshold average or median frequency or local
average or median
frequency is reached.
1000361 In .the
step 36, data may be analyzed to determine whether a. pattern or distinctive
point is present in the audio data. For example, a distinctive point may be
identified based on. meeting
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one or more criteria such as meeting criteria as a local maximum amplitude
value or local maximum
amplitude value meeting sonic threshold. In the step 38. the periodicity of a
plurality of identified
patterns or points over time may be examined.
1000371 in a
step 38, one or more times between. identified points of a detected pattern
may be determined. .For example, it may be determined .that a 0.5 second
period of time elapsed
between data intervals identified as meeting a certain threshold because the
points satisfy the
condition of being threshold local amplitude maximum values. In a step 40 an
algorithm may analyze
whether the times are indicative of a seizure. For example, in some
embodiments, a time period may
.be identified as indicative of a seizure if the period is between about 0.2
to about 2 seconds, An
algorithm may be tuned so that any number of suitable time periods must be
identified. before a
seizure .is indicated, For example. the period between 2 or more identified
points or detected patterns
may be determined,. and as a greater number of suitable periods are measured
the algorithm may.
indicate a higher proba.bility that a seizure may be occurring. For example,
in some embodiments, an
algorithm may initiate an alarm until at least about 4 to about 1.0 patterns
are identified. The regularity
of duration or regalarity of time periods may further be artalyzed in an.
algorithm. -For example, a
standard deviation or other statistical metric associated with multiple
periods may be used to analyze
whether the determined periods are suitably periodic.
1000381 By way
of example only, if over a monitoring period a patient inhales and
exhales 10 times and if at times near when the patient begins a cycle of
inhalation air being carried
into the lungs a recognizable sound is produced that sound may be
characterized such as by amplitude
and or frequency (e.g.:, a part in the cycle of inhalation and exhalation may
be paled out or detected
from other points) and identified as a point in a. seizure related pattern.
With 10 cycles there may be 9
periods between identified points (which in this example is a recognized sound
produced during
inhalation as a patient gasps tbr air). That recognized sound may, for
example, include a local
maximum in amplitude, at a certain time or may be characterized in other ways.
For example. the
times identified may conveniently be characterized by subscripts as follows:
,17.4
Relative periods between the identified times may then be calculated as
follows:
T.2T AT
T3 ¨
T1.3¨ T9 = A
And, any of various procedures may then be -used to determine one or more
metrics of how periodic or
regular in time the periods may be. For example, in one embodiment. time
periods between .identified
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points may be determined (as above) and an average time period may then be
calculated. The average
time period. may be compared to individually measured time periods (04,, how
much deviation from
the average period is present) and a standard, relative, or petee/I18ge
deviation then determined. For
example, a processor may execute ea/cub:lions as follows:
Average time period ,e (al .+ ATe+ + AT)/(W9) INT*0
Average Deviation = Individual Deviations / No. Deviations
I
AT ----- (ATI ¨ Nve) Ft.. I (AT, eee)
Percentage deviation =[Average Deviation I AT (066J x 100%
1000391 A
percentage deviation may, for example, be compared to one or more threshold
.values of .percentage deviation, and if the percentage deviation MCCiSthc
threshold criteria, periodicity
of the detected pattern (e.g., series of 10 inhalation and exhalation
producing .10 repeating patterns in
the above example) may be viewed as indicative of seizure activity. For
example, if the periodicity
requirement is fulfilled then an alarm or other response may be executed. Al3
algorithm may, in sonic
embodiments, include comparison of a percentage deviation to one or more
threshold values including
a minimum percentage deviation and/or a maximum percentage: deviation. For
example, a repeating
.noise source that is artificially periodic may show very low percentage
deviation and may not be
deemed indicative of a seizure. However, human speech which may be more random
than sounds
made during a seizure .may be less periodic. And. le some embodiments, an
audio detection method
inay include comparison of data to both a nimimum and/or maximum percentage
deviation (or other
suitable metric of .periodicity) and comparison to a minimum and/or maximum
period. For example.
where a portion of audio data has a. pattern that repeats within threshold for
percentage deviation (e.g.,
meeting minimum and maximum thresholds for periodieny) and where the portion
of audio data
includes a pattern that repeats between sonic minimum and maximum number of
times per second the
audio data may be deemed indicative of a. seizure.
[000A In some
embodiments, acoustic data may be used individually to trigger an alarm
state. However, in some embodiments, a detection algorithm may also analyze
(as shown in a step 42)
whether other sensor data (e.g.. FAIG data) supports a finding that a seizure
may be present. For
example, .if acoustic data is collected and it is determined that the data is
characteristic of a seizure
and in the same time period threshold E.MG values area also satisfied a method
30 may deem certainty
of seizure detection to he high and may initiate an alarm protocol in a step
44. In some embodiments,
acoustic data may be weighted together with EMG data to determine the
likelihood that a seizure may
be present. And, in some embodiments, acoustic data may be used to corroborate
a finding that weak
motor manifestations are indicative of seizure activity. In some embodiments,
audio data may act as
input in a supervisory algorithm as described in Applicant's related co-
pending application No.
13/275,309 filed October 17, 2011 and herein incorporated by reference. For
some patients. a
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temporal delay between audible manifestations of a seizure and muscular
manifestations of a seizure
.may sometimes occur, and a time period in which the EMG and acoustic data are
determined to be
related mat, be adjusted accordingly.
14.100411 In some
embodiments, a seizure detection algorithm may include inputs from
each of one or more EMG sensors and one or more acoustic sensors, and for
example, if sensors of
both types exceed appropriate threshold levels an alarm state be triggered.
Some of those
embodiments may monitor the periodicity of detected acoustic patterns and/or
may integrate other
signatures of acoustic data. Figure 4 illustrates an. exemplary method 50
wherein an alarm may be
initiated if, for example, each of an EMG and acoustic sensor exceed
respective threshold levels
during a. certain time period. In a step 52, a patient may be monitored using
a combination of EMG
and acoustic sensors, and the method may look for a first threshold detection
event.. If either sensor
exceeds a threshold, the .method may, as shown in a step 54, establish a time
period for the monitoring
of a threshold event of the other sensor type and continue to monitor the
patient. For exampleõ if a first
event is the exceeding of an .ENIG threshold the method may establish a period
following that event
wherein threshold detection of an audio signal may trigger an. alarm.
Therefore, as Shown in a step 56,
a method 50 may determine whether threshold detection of both an EMG and
acoustic sensor was met
within the established time period. .lf both threshold EMG and threshold
acoustic events were
satisfied, as shown in step 58, an alarm protocol may be initiated.
Alternatively, if no corroborating
event was detected, the system may return to monitoring a patient for a next
threshold event. For
example, a method 50 may require: that one event is detected and that a
.corroborating event is detected
within a time period of up to about 2 'minutes or up to about 5 minutes.
1000421 For some
patients, sounds produced during One part of a seizure may be different
.than produced during other parts of a seizure. For example, for some
patients, alien times during a
tonic portion of a seizure a patient may rapidly exhale sometimes with a loud
scream. The patient may
not inhale and begin rhythmic breathing for some period of time. For example,
during or after onset of
the ionic phase the patient may resume inhaling and at some time the patient
may begin to
repetitively produce a sound pattern often times as they attempt to regain
stable breathing. Some
methods herein may look at audio data over time and by identifying features
typical of various parts
of a seizure those features may be analyzed together to increase confidence in
seizure detection. For
example, a method of monitoring a patient may include analyzing collected
audio data for a high
amplitude scream or sound typical of the onset of a seizure and then track the
data to look for patterns
of an attempt to regain stable breathing. For example, if a high amplitude
scream is followed by lower
amplitude audio signals for some characteristic time and then followed by a
repetitive pattern (such as
discussed above with respect to Figure 4), a seizure may he deemed present and
an alarm or other
response imitated,
I004.1431
Moreover, in some embodiments of methods of detecting a seizure, audio data
may be collected. along with other sensor data. ff trends in the audio data
seem to indicate transition
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between more than one part of a seizure (such as discussed above), and if the
other sensor data
corroborates those transitions confidence or seizure detection may be greatly
improved. For example.
in sonic embodiments, more than one electromyography routine may be executed
together with
collection of audio signal, and the plurality of data may be used to not only
detect a seizure, but to
also to track changes in seizure activity during transition between one or
more seizure phases. Various
applications associated with the treatment or termination of seizures (e.g.,
such as may include Vagal
nerve stimulation), selective collection or transmission of additional, sensor
data. and/or selective and
customized responses to a detected seizure condition may benefit from the
detection and tracking of
changes in seizure activity as described herein.
1000441 In, some
embodiments, a method. of monitoring a patient for seizure a.etivity may
include a first EMG routine that is highly responsive to initial motor
manifestations and/or tonic
activity and a. second EMU routine may be selective, for elonic-phase
activity. Routines that may be
made responsive or selective fix detection of initial motor manifestations
typical of seizure activity or
for different phases of a seizure are, for example, described in Applicant's
Co-pending Provisional
Application No. 62/001,302 filed May 21, 2014 and also in Applicant's Co-
pending Provisional
Application 'NO. 62/032,147 filed August I, 2014 the disclosures of which are
herein incorporate,d by
reference.
1000451 For
example. a routine that may be responsive to initial motor manifestations
and/or tonic activity may include collecting EVIG signals over some period of
time and integrating the
amplitude of collected signals within OM or rthlre consecutive or overlapping
timc windows within
that .period. and .then determining if the integrated amplitude was elevated
over a certain threshold for.
some time as may, for example, be determined ii the threshold is met
consistently or with sonic
probability over a number of time windows. Levels of EMG signal amplitude may
be calculated front
signal collected in one or mote frequency bands and appropriate filters may be
used to isolate one or
:more target frequency bands. Threshold levels of integrated EMG signal
amplitude and/or
.requirements that a threshold value is maintained for a period of thBe may,
in some embodiments, be
set to make that routine responsive to motor manifestations that may be weaker
than typically found
in a seizure or in a seizure that is likely to be dangerous. Integration time
windows may be established
to improve detection of relatively weak motor manifestations, For example, in
some embodiments,
integration time windows for EMU signal collection may be of duration or at
least about 20
milliseconds. at least about 50 milliseconds, or at least about 100
milliseconds,
1000461 In some
embodiments, a threshold level of .ENIG signal amplitude may be made
based on a measurement of a signal amplitude an individual may provide during
a voluntary muscle
contraction. And. in some embodiments, to capture weak motor manifestations a
value of about 2% to
about 50% of a maximum voluntary contraction value may be set.
1004.147j Also by
way of example, a routine .that may he selective for cionic phase activity
may include determining if a portion of EM.Ci data includes elonic-phase
bursts as may be based on
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fldfilling of a minimum burst width and/or maximum burst Nvidth criterion, and
if some number of
bursts are detected the routine may deemed responsive and clonicephase
activity detected. That is, a
routine may count bursts or determine a burst rate and if the number or rate
exceeds a threshold a
positive response may be logged. in some embodiments, a burst envelope may be
generated and the
burst envelope may impact a SNR threshold that may be used to identify bursts.
For example, with a
simple peak detect method, bursts may be qualified by meeting a threshold SNR
of about L25 to
about 20 and by meeting a minimum threshold for burst width of about 25 to
.about 75. milliseconds
and maximum burst Ividth threshold of no greater than about 250 milliseconds
to about 400
.milliseconds. Bursts may .then be counted and a number of bursts or rate of
bursts may be determined.
For example, a positive routine response may then, for some patients, be
triggered if between about 2
to about 6 'bursts are measured within a time window of about I second or if'
another suitable number
of bursts are counted in some other appropriate time window.
1000481 A method
60 of monitoring a patient for seizure characteristics which may
include collection and processing or processing of both audio and ENIG data is
shown in Figure 5.
Like the method 50, the presence of both audio and BAG data may generally
increase confidence that
a seizure is pwsent. However, in the method 50, it may, in some embodiments,
only be required that
a seizure event is detected and a corroborating event also detected. For
example, one event may be
based on ENT0 data and a corroborating event may be audio data. And, if one
event is detected and a
corroborating, detection made without about 2 minutes the events may be deemed
to be corroborated
and an alarm may be initiated.
1000491 To
improve detection efficiency, in the method 60, particular routines are Den
that
individually or in combination may facilitate selective detection of one or
more seizure phases or
parts. That is. for example, and first considering EM,Ci data, a combination
of the aforementioned
exemplary routines may be executed. And, if those ENIG routines are
individually responsive to a
given part of a seizure an alarm may be triggered in some patients, 'Where
both routines affirm seizure
activity an alarm may also be triggered as confidence in seizure detection and
seizure seventy may be
high, For example, selective detection of elonic activity .may be related to
adverse effects of a. seizure
and generally an emergency response may be executed if a tonic-ClarliC seizure
is detected. Where
detection of weak motor manifestations Of tonic-phase activity is lollowed by
selective detection of
elonic-phase activity the pattern of detections may increase confidence that a
seizure was detected and
may further be used to classify the seizure as a classic mniceelonic seizure
event.
10005in Next,
considering audio data, in one routine sound energy may be collected and
processed to identify the presence of both high amplitude signals that may be
typical of a scream near
the start of a seizure and in a second audio detection routine data may be
examined for the. presence of
:repetitive patterns that may, for example, be indicative of a person gasping
liar air as they attempt to
deal with or recover from a seizure. in some embodiments, a routine for
looking at audio data may
also or alternatively identify sounds produced indirectly from a patient
struggling during a seizure.
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For example, a routine may examine audio data for signs that furniture or a
sound device is
.rhythinically moving. Again. where more than one feature of activity is
present: (e.g., where both
routines indicate the presence of signatures of seizure activity) likelihood
that a seizure is present is
high and an alarm may be triggered. To improve confidence a routine may look
for a characteristic lag
between the various aspects of audio data. For example, where a repetitive
sound pattern is temporally
correlated (e.g., separated by an expected time front a scream confidence of
detection may be
increased, For example, if a scream, commonly indicative of tonic activity, is
detected and a repetitive
sound pattern is then identified (either from gasping or rhythmic movement of
furniture or a sound
device) within about 5 to about 45 seconds .confidenee of seizure detection
may he improved. And,
the COM bination may be selectively characterized as a tonic-clonic seizure.
[WWII By way
of contrast with the method 50, the method 60 may improve detection
efficiency by considering in a detection algorithm a temporal relationship
between. various routine that
inthsiduailv or in combination are selective for one or more parts of a
seizure. And, importantly,
where two routine for the same part are detected at about the same time the
detections may be
weighted appropriately. For example. if detections in two routines are made,
and where the routines
are both selective for times near the start of a seizure the detections may be
super-linearly weighted.
That is, if the two detections are made and correlated in time contribution of
the events to seieure
detection may be accordingly adjusted. For example, in some embodiments, the
detections may be
contribute .nonlinearly (or super-additivety), in some embodiments, if the
detections are made bet not
correlated in time, the events .may still be, included in an algorithm to
detect a seizure, but ordy with a
reduced weight. Alternatively, it may be required that temporal coherence
between the events is
.maintained. That is, without being correlated the, detections may be
discounted. Because the various
routines may be correlated with the same part of a seizure, requirements for
temporal coherence may.
be strict and risk of incorrectly identifying a seizure may accordingly
minimized,
10ff0521
Referring back to Figure 5, in a step 62, audio and F.MG data may be collected
and processed. Alternatively, the method 60 may comprise a. method of
analyzing sensor data. That is.
the sensor data may be collected separately and the method 60 may be used to
analyze the data for a
seizure event. in the method 60, a plurality of routines may run together. The
routines may
individually or in combination be selective for one part of a seizure and the
method. may weigh
various detections in a manner based on the expected tinting for the various
responses as expected in
an actual seizure. For example, M. some embodiments, each of a first routine
and second routine for
detection using .EMG (including those described above) may be run. together
(in the step 62) and
various algorithms may probe the data for either isolated parts of a seizure
and/or for various multi-
part seizure events. Likewise, as also shown in the step 62, each of a first
and second routine for
detection of audio signatures of a seizure may be executed. For example, one
routine mar analyze
collected audio data looking for the occurrence of high amplitude audio data
that may indicate the
presence of an audible scream as may occur near the start of a seizure and a
second audio routine may
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look .for repetitive data indicative of later portions of a seizure or of
seizure recovery.
1000531 A first
routine for .EMO detection may look for tonic phase activity or pre-seizure
activity. Where an audible scream is correlated in time with EMG detection of
tonic-phase activity the
relative detections may be combined in an algorithm for seizure detection.
Particularly, in some
embodiments. the relative weight of the detections (step 64) may be added in a
sup:or-linear manner;
.that is, in the above example not only were both detections (EM G and audio)
made, but the detections
were made with temporal coherence in an expected manner and because the parts
are often related to
the same part of a seizure increase confidence in seizure detection may be
particularly high. That is,
audio and EMG events expected to occur at about the .time were made and the
signals temporally
correlated. In some embodiments, routines :for identification of early seizure
or tonic phase activity
using EMC and routines for detecting an initial high amplitude scream may be
deemed temporally
correlated and weighted in an algorithm for seizure detection if the events
occur within about I
minute from each.
1000541
Likewise, an algorithm may analyze collected audio data looking for the
occurrence of repetitive audio data that may, for example, indicate the
presence of a patient
attempting to regain control of respiration or inducing rhythmic movement of
sound, and that may
occur after initial, manifestations of a seizure. In addition, an algorithm
may analyze EMG data using
one or more routines selective for clonicaphase activity andior for E.M0 data
associated with post-
seizure recovery. For some patients, the presence of chink-phase bursts and
the presence gasping of
air may be biarhly correlated. And, M. some embodiments. routines ibr
identification of cloak, phase
activity using -EMG and routines for detecting repetitive gasps may be deemed
.temporally correlated
and weighted in an algorithm for seizure detection if the events occur within
about 30 seconds of each
other. Moreover, for some patients trends in periodicity for the
aforementioned audio routine and
EMU detection routine may be. highly coirelated. For example, patient motor
.manifestations as
measured in WC" and patient audio responses (e.g., gaspiag) may be related.
1000551 In some
embodiments, a threshold level of activation of an acoustic sensor may-
be based on a Level that is some number of standard deviations above a
baseline level collected for an
acoustic sensor during a non-seizure reference period. Alternative, in some
embodiments. a
threshold level of audio activation may be set based on a ratio between an
acoustic sensors baseline
level, and a threshold noise level. For example, a threshold level of an
acoustic sensor may be reached
upon an increase in acoustic signal of about 10 decibels to about 40 decibels
above the acoustic
sensors measured baseline level. In other embodiments, a threshold !evel of
activation for an acoustic
sensor .may be defined based on a sensor reaching a certain decibel level
above a standard reference
value. An acoustic sensor may, for example. be calibrated against a 0 db
signal such as may be
typically Meaatred using an external pressure of about 20 iiiie,ropaseals, la
some embodiments, a
threshold level of activation of an acoustic sensor may be met if the acoustic
sensor measures sound at
a level exceeding about 5.0 decibels or about 75 decibels. In some
embodiments. a threshold level of
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audio activation may be high enough that normal speech may not exceed the
threshold, but a scream,
as may. be typical of some patients experiencing a seizure, may exceed a
threshold level of activation.
1000561 A
threshold value of EMC1 activity may be based on any of various
characteristics
of EMG activity including for example a T-squared statistical value, presence
of amplitude bursts or
combinations of EM.G. characteristics thereof. lu some embodiments. VAG
signals may be collected
.for a .time period and processed by filtering to select a plurality of
frequency bands. For example, an
EMG frequency spectrum may be broken up into a number of frequency bands, such
as throe or more,
and one or more characteristics of each frequency band, for example. power
content of the band or
spectral density at one or more frequencies within .the band, may be measured,
A measured
characteristic for a frequency band may be normalized by its variance and
covariance with respect to
the characteristic as measured in other frequency bands and resulting
normalized values processed to
detennine one or more T-squared statistical value. A T-squared statistical
value may be compared to a
reference T-squared statistical value and if the T-squared value exceeds the
reference value a
threshold condition may be satisfied. In some embodiments, 'f-squared
reference values may be
established using one or more reference and/or training periods. RIF example,
a reference T-squared
value may be a number of standard deviations from a T-squared baseline
obtained while a patient may
be resting. IR other embodiments, a reference I-squared value may be scaled
based on a measurement
obtained while a .patient may be executing a maximum voluntary contraction
anti/or may be calculated
based on a patients mid-upper arm circumference.
1000571 In some
embodiments, initiation, of an alarm protocol may be dependent upon
meeting threshold levels of both audio and EMG activity within a certain
period of time. For example,
to eliminate false positive detection of a seizure based upon audio signals
occurring from non-seizure
events, which may also he loud, EMC activation may be required to occur in
addition to audio
detection, and only if both threshold events occur in an established time
period an alarm protocol may
be initiated. Temporal correlation of EM C1 activation and audio activation
may be adjusted ibr an
individual patient: or patient group,
1000581 In some
embodiments, data from one or more acoustic MIMI'S may be used along
with other data from one or more other sensors in a method of seizure
detection. For example, audio
data may be collected as part or a sub-method in an algorithm configured to
periodically probe data
from an acoustic sensor and look for periods of' high amplitude signals. If
detected, the sub-method
may increase the value of a register and periodically transfer the registers
contents to an accumulation
register. .An accumulation register may therefore serve as a metric of
acoustic activity. An
accumulation register may be periodically adjustal (e.g., incremented or
decremented) at a desired
rate and thereby configured such that only recent acoustic data is held.
TherefOre, if during a certain
time period acoustic activity is high, the accumulation .register may tend to
increase in value. Other
sub-methods, such as more thoroughly described in U.S. Patent Applications
Nos. 13/275,309 and
13/542,596, may also be operating and may act as sentinels of different
characteristics of EMG data.
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Periodically, a supervisory algorithm may analyze the contents of one or more
accumulation registers
to determine whether a seizure is likely occurring. If the supervisory
algorithm determines that the
sum of values or a weighted sum of µ,aities in the accumulation registers
exceeds a threshold then an
alarm protocol may be initiated.
1000591 lu some
embodiments, a plurality of audio sensors may be present in a
monitoring region and sounds originating within or near the region may be
detected by different
sensors. Variation among the detected signals may be used to spatially filter
sound components. For
example, spatial filtering of audio data may be used in combination with data
associated with an
expected or measured position of a patient, For example, sound components
likely originating from a
location that is spatially distinct from the patient may be discounted or
weighted by a factor that
decreases the significance of a sound or sound component used in a seizure
detection alaorithin. in
some embodiments, one or more environmental transceivers may be placed in a.
detection area and as
a patient moves the relative position of a patient may be established.
1000601 In some
embodiments, acoustic data may be analyzed in real-time and integrated
in an algorithm for determining whether to initiate an alarm protocol.
Analysis of acoustic data may
he fully or semi-automated. For example, in some embodiments, acoustic data
may include amplitude
data or normalized data, and may be integrated into a detection algorithm
without the need for.
interpretation by care-giver personnel. However, in some embodiments, audio
data may also be sent
.to a care-giver during or after a seizure. For example, in some embodiments,
audio data or audio data
correlating with possible seizure activity may be sent to remote personnel
trained to take appropriate
action.. In some embodiments, data sent: to remote personnel may be compressed
to reduce
-transmission bandwidth or processed to encourage efficient analysis by care-
giver personnel,. For
example., audio andfor ENIG data may be suitably compressed so that the
information may be readily
scrolled through during analysis.
[WWII In some
embodiments, detection of a seizure or possible seizure related event
.may trigger automatic transmission of EN-IC and audio data to a remote
monitoring facility.. For
example, if an alarm is triggered data proceeding and after the event may be
sent for ieview. In some
embodiments, .E1\40 data may be decimated to reduce the size of the file, but
not decimated so much
as to lose visible quality, Reduction or the file may, for example, make it
more responsive when
manipulating the data from a local computer with internet service. A caregiver
viewing the data on a
local computer may then select to view/listen to any portion of the
transmitted data. In one
embodiment, a five minute interval on either side of arm expected event (e.g.
10 minutes of data) may
be sent and/or uploaded for review. A care-giver viewing the data on a local
computer may select to
view/listen to the entire ten minutes or select on a series of buttons labeled
1-10 to view/ listen at a
particular 1 minute segment. The software may be configured such that a
selected portion of EMO
data may scroll across the screen at a rate such that associated audio data
(e,g,, data collected at the
same time as the EMC data) is simultaneously heard..
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1000621 Although
the disclosed method and apparatus and their advantages have been
described in detail, it should be understood that various changes,
substitutions and alterations can be
made herein without departing from the invention as defined by the appended
claims. Moreover, the
scope of the present application is not intended to be limited to the
particular embodiments of the
process, machine, manufacture, composition, or matter, means, methods and
steps described in the
specification. Use of the word "include,' for example, should be interpreted
as the word
"comprising" would be, i.e., as open-ended. As one will readily appreciate
from the disclosure,
processes, machines, mantacture, compositions of' matter, means, methods, or
steps. presently
existing or later to be developed that perform substantially the same function
or achieve substantially
the same result as the corresponding embodiments described herein may be
utilized. Accordingly, the
appended claims are intended to include within their scope such processes,
machines, manufaeture,
compositions of inattel; means, methods or steps.
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