Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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SYSTEM FOR IDENTIFYING CARDIAC CONDUCTION PATTERNS
RELATED APPLICATIONS
[0001] The present application claims priority under 35 USC 119(e) to
United States
Provisional Patent Application No. 62/619,897, entitled "System for
Recognizing Cardiac
Conduction Patterns," filed January 21, 2018, and United States Provisional
Patent
Application No. 62/668,647, entitled "System for Identifying Cardiac
Conduction Patterns,"
filed May 8, 2018, each of which is incorporated herein by reference in its
entirety.
[0002] The present application, while not claiming priority to, may be
related to U.S.
Provisional Patent Application No. 62/757,961, entitled "Systems and Methods
for
Calculating Patient Information," filed November 9, 2018, which is hereby
incorporated by
reference.
[0003] The present application, while not claiming priority to, may be
related to U.S.
Provisional Patent Application No. 62/668,659, entitled "Cardiac Information
Processing
System," filed May 8, 2018, which is hereby incorporated by reference.
[0004] The present application, while not claiming priority to, may be
related to US
Patent Application No. 16/097,959, entitled "Cardiac Mapping System with
Efficiency
Algorithm," filed October 31, 2018, which is a 35 USC 371 national stage
filing of Patent
Cooperation Treaty Application No. PCT/U52017/030922, entitled "Cardiac
Mapping
System with Efficiency Algorithm", filed May 3, 2017, which claimed priority
to US
Provisional Patent Application No. 62/413,104, entitled "Cardiac Mapping
System with
Efficiency Algorithm," filed October 26, 2016 and US Provisional Patent
Application No.
62/331,364, entitled "Cardiac Mapping System with Efficiency Algorithm," filed
May 3,
2016, each of which is hereby incorporated by reference.
[0005] The present application, while not claiming priority to, may be
related to US
Patent Application No. 16/097,955, entitled "Cardiac Information Dynamic
Display System
and Method," filed October 31, 2018, which is a 35 USC 371 national stage
filing of Patent
Cooperation Treaty Application No. PCT/US2017/030915, entitled "Cardiac
Information
Dynamic Display System and Method", filed May 3, 2017, which claims priority
to US
Provisional Patent Application No. 62/331,351, entitled "Cardiac Information
Dynamic
Display System and Method", filed May 3, 2016, each of which is hereby
incorporated by
reference.
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[0006] The present application, while not claiming priority to, may be
related to Patent
Cooperation Treaty Application No. PCT/ US2017/056064, entitled "Ablation
System with
Force Control", filed October 11, 2017, which claims priority to US
Provisional Patent
Application No. 62/406,748, entitled "Ablation System with Force Control",
filed October
11, 2016, and US Provisional Patent Application No. 62/504,139, entitled
"Ablation System
with Force Control", filed May 20, 2017, each of which is hereby incorporated
by reference.
[0007] The present application, while not claiming priority to, may be
related to US
Application No. 15/569,457, entitled "Localization System and Method Useful in
the
Acquisition and Analysis of Cardiac Information," filed October 26, 2017,
which is a 35 USC
371 national stage filing of Patent Cooperation Treaty Application No.
PCT/US2016/032420,
entitled "Localization System and Method Useful in the Acquisition and
Analysis of Cardiac
Information", filed May 13, 2016, which claims priority to US Provisional
Patent Application
No. 62/161,213, entitled "Localization System and Method Useful in the
Acquisition and
Analysis of Cardiac Information", filed May 13, 2015, each of which is hereby
incorporated
by reference.
[0008] The present application, while not claiming priority to, may be
related to US
Patent Application No. 15/569,231, entitled "Cardiac Virtualization Test Tank
and Testing
System and Method," filed October 25, 2017, which is a 35 USC 371 national
stage filing of
Patent Cooperation Treaty Application No. PCT/US2016/031823, entitled "Cardiac
Virtualization Test Tank and Testing System and Method", filed May 11, 2016,
which claims
priority to US Provisional Patent Application No. 62/160,501, entitled
"Cardiac
Virtualization Test Tank and Testing System and Method", filed May 12, 2015,
each of
which is hereby incorporated by reference.
[0009] The present application, while not claiming priority to, may be
related to US
Application No. 15/569,185, entitled "Ultrasound Sequencing System and
Method," filed
October 25, 2017, which is a 35 USC 371 national stage filing of Patent
Cooperation Treaty
Application No. PCT/U52016/032017, entitled "Ultrasound Sequencing System and
Method", filed May 12, 2016, which claims priority to US Provisional Patent
Application No.
62/160,529, entitled "Ultrasound Sequencing System and Method", filed May 12,
2015, each
of which is hereby incorporated by reference.
[0010] The present application, while not claiming priority to, may be
related to US
Application No. 14/916,056, entitled "Devices and Methods for Determination of
Electrical
Dipole Densities on a Cardiac Surface", filed September 10, 2014, which is a
35 USC 371
national stage filing of Patent Cooperation Treaty Application No.
PCT/1J52014/54942,
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entitled "Devices and Methods for Determination of Electrical Dipole Densities
on a Cardiac
Surface", filed September 10, 2014, which claims priority to US Provisional
Patent
Application No. 61/877,617, entitled "Devices and Methods for Determination of
Electrical
Dipole Densities on a Cardiac Surface", filed September 13, 2013, each of
which is hereby
incorporated by reference.
[0011] The present application, while not claiming priority to, may be
related to US
Application No. 15/128,563, entitled "Cardiac Analysis User Interface System
and Method",
filed September 23, 2016, which is a 35 USC 371 national stage filing of
Patent Cooperation
Treaty Application No. PCT/US2015/22187, entitled "Cardiac Analysis User
Interface
System and Method", filed March 24, 2015, which claims priority to US Patent
Provisional
Application No. 61/970,027, entitled "Cardiac Analysis User Interface System
and Method",
filed March 28, 2014, each of which is hereby incorporated by reference.
[0012] The present application, while not claiming priority to, may be
related to US
Application No. 16/111,538, entitled "Gas-Elimination Patient Access Device",
filed August
24, 2018, which is a continuation of US Patent No. 10,071,227, entitled "Gas-
Elimination
Patient Access Device", filed January 14, 2015, which was a 35 USC 371
national stage
filing of Patent Cooperation Treaty Application No. PCT/US2015/011312,
entitled "Gas-
Elimination Patient Access Device", filed January 14, 2015, which claimed
priority to US
Provisional Patent Application No. 61/928,704, entitled "Gas-Elimination
Patient Access
Device", filed January 17, 2014, each of which is hereby incorporated by
reference.
[0013] The present application, while not claiming priority to, may be
related to US
Patent Application No. 16/242,810, entitled "Expandable Catheter Assembly with
Flexible
Printed Circuit Board (PCB) Electrical Pathways", filed January 8, 2019, which
is a
continuation of Patent Application No. 14/762,944, entitled "Expandable
Catheter Assembly
with Flexible Printed Circuit Board (PCB) Electrical Pathways", filed July 23,
2015, which
was a 35 USC 371 national stage filing of Patent Cooperation Treaty
Application No.
PCT/US2014/15261, entitled "Expandable Catheter Assembly with Flexible Printed
Circuit
Board (PCB) Electrical Pathways", filed February 7, 2014, which claims
priority to US
Provisional Patent Application Serial No. 61/762,363, entitled "Expandable
Catheter
Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways", filed
February 8,
2013, which is hereby incorporated by reference.
[0014] The present application, while not claiming priority to, may be
related to US
Patent Application No. 16/012,051, entitled "Catheter, System and Methods of
Medical Uses
of Same, Including Diagnostic and Treatment Uses for the Heart," filed June
19, 2018, which
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is a continuation of US Patent No. 10,004,459, entitled "Catheter, System and
Methods of
Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart",
filed
February 20, 2015, which is a 35 USC 371 national stage filing of Patent
Cooperation Treaty
Application No. PCT/U52013/057579, entitled "Catheter System and Methods of
Medical
Uses of Same, Including Diagnostic and Treatment Uses for the Heart", filed
August 30,
2013, published as WO 2014/036439, which claims priority to US Provisional
Patent
Application No. 61/695,535, entitled "System and Method for Diagnosing and
Treating Heart
Tissue", filed August 31, 2012, each of which is hereby incorporated by
reference.
[0015] The present application, while not claiming priority to, may be
related to US
Design Patent Application No. 29/593,043, entitled "Set of Transducer-
Electrode Pairs for a
Catheter," filed February, 6, 2017, which is a divisional of US Design Patent
No. D782686,
entitled "Transducer Electrode Arrangement", filed December 2, 2013, which is
a
continuation-in-part of Patent Cooperation Treaty Application No.
PCT/U52013/057579,
entitled "Catheter System and Methods of Medical Uses of Same, Including
Diagnostic and
Treatment Uses for the Heart", filed August 30, 2013, which is hereby
incorporated by
reference.
[0016] The present application, while not claiming priority to, may be
related to US
Patent Application No. 15/926,187, entitled "Device and Method for the
Geometric
Determination of Electrical Dipole Densities on the Cardiac Wall," filed March
20, 2018,
which is a continuation of US Patent No. 9,968,268, entitled "Device and
Method for the
Geometric Determination of Electrical Dipole Densities on the Cardiac Wall,"
which is a
continuation of US Patent No. 9,757,044, entitled "Device and Method for the
Geometric
Determination of Electrical Dipole Densities on the Cardiac Wall", which is a
35 USC 371
national stage filing of Patent Cooperation Treaty Application No.
PCT/U52012/028593,
entitled "Device and Method for the Geometric Determination of Electrical
Dipole Densities
on the Cardiac Wall," filed March 9, 2012, which claimed priority to US
Provisional Patent
Application No. 61/451,357, entitled "Device and Method for the Geometric
Determination
of Electrical Dipole Densities on the Cardiac Wall," filed March 10, 2011,
each of which is
hereby incorporated by reference.
[0017] The present application, while not claiming priority to, may be
related to US
Patent Application No. 15/882,097, entitled "Device and Method for the
Geometric
Determination of Electrical Dipole Densities on the Cardiac Wall," filed
January 29, 2018,
which is a continuation of US Patent No. 9,913,589, entitled "Device and
Method for the
Geometric Determination of Electrical Dipole Densities on the Cardiac Wall",
filed October
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25, 2016, which is a continuation of US Patent No. 9,504,395, entitled "Device
and Method
for the Geometric Determination of Electrical Dipole Densities on the Cardiac
Wall", filed
October 19, 2015, which is a continuation of US Patent No. 9,192,318, entitled
"Device and
Method for the Geometric Determination of Electrical Dipole Densities on the
Cardiac Wall",
filed July 19, 2013, which is a continuation of US Patent No. 8,512,255,
entitled "Device and
Method for the Geometric Determination of Electrical Dipole Densities on the
Cardiac Wall",
issued August 20, 2013, which was a 35 USC 371 national stage application of
Patent
Cooperation Treaty Application No. PCT/IB09/00071 filed January 16, 2009,
entitled "A
Device and Method for the Geometric Determination of Electrical Dipole
Densities on the
Cardiac Wall", which claimed priority to Swiss Patent Application No. 00068/08
filed
January 17, 2008, each of which is hereby incorporated by reference.
[0018] The present application, while not claiming priority to, may be
related to US
Patent Application No. 16/014,370, entitled "Method and Device for Determining
and
Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed June
21, 2018,
which is a continuation of US Patent Application No. 15/435,763, entitled
"Method and
Device for Determining and Presenting Surface Charge and Dipole Densities on
Cardiac
Walls," filed February 17, 2017, which is a continuation of US Patent No.
9,610,024, entitled
"Method and Device for Determining and Presenting Surface Charge and Dipole
Densities on
Cardiac Walls", filed September 25, 2015, which is a continuation of US Patent
No.
9,167,982, entitled "Method and Device for Determining and Presenting Surface
Charge and
Dipole Densities on Cardiac Walls", filed November 19, 2014, which is a
continuation of US
Patent No. 8,918,158, entitled "Method and Device for Determining and
Presenting Surface
Charge and Dipole Densities on Cardiac Walls", issued December 23, 2014, which
is a
continuation of US Patent No. 8,700,119, entitled "Method and Device for
Determining and
Presenting Surface Charge and Dipole Densities on Cardiac Walls", issued April
15, 2014,
which is a continuation of US Patent No. 8,417,313, entitled "Method and
Device for
Determining and Presenting Surface Charge and Dipole Densities on Cardiac
Walls", issued
April 9, 2013, which was a 35 USC 371 national stage filing of PCT Application
No.
CH2007/000380, entitled "Method and Device for Determining and Presenting
Surface
Charge and Dipole Densities on Cardiac Walls", filed August 3, 2007, which
claimed priority
to Swiss Patent Application No. 1251/06 filed August 3, 2006, each of which is
hereby
incorporated by reference.
Field of the Invention
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[0019] The present invention is generally related to systems and methods
that may be
useful for the diagnosis and treatment of cardiac arrhythrnias or other
abnormalities, in
particular, the present invention is related to systems, devices, and methods
useful in
displaying cardiac activities associated with diagnosing and treating such
arrhythmias or
other abnormalities.
BACKGROUND
[0020] Cardiac signals (e.g. charge density, dipole density, voltage, etc.)
vary across the
endocardial surface in magnitude. The magnitude of these signals is dependent
on several
factors, including local tissue characteristics (e.g. healthy vs.
disease/scar/fibrosis/lesion) and
regional activation characteristics (e.g. "electrical mass" of activated
tissue prior to activation
of the local cells). A common practice is to assign a single threshold for all
signals at all
times across the surface. The use of a single threshold can cause low-
amplitude activation to
be missed or cause high-amplitude activation to dominate/saturate, leading to
confusion in
interpretation of the map. Failure to properly detect activation can lead to
imprecise
identification of regions of interest for therapy delivery or incomplete
characterization of
ablation efficacy (excess or lack of block).
[0021] The continuous, global mapping of atrial fibrillation yields a
tremendous volume
of temporally- and spatially-variable activation patterns. A limited, discrete
sampling of map
data may be insufficient to provide a comprehensive picture of the drivers,
mechanisms, and
supporting substrate for the arrhythmia. Clinician review of long durations of
AF can be
challenging to remember and piece together to complete the "bigger picture."
[0022] For these and other reasons, there is a general need to
algorithmically provide an
objective analysis of conduction patterns.
SUMMARY
[0023] Embodiments of the systems, devices and methods described herein can
be
directed to systems, devices and methods for diagnosing an arrhythmia of a
patient.
[0024] According to an aspect of the present inventive concepts, a system
for diagnosing
an arrhythmia of a patient comprises: a diagnostic catheter for insertion into
the heart of the
patient, and a processing unit. The diagnostic catheter is configured to
record anatomic and
electrical activity data of the patient. The processing unit is configured to
receive the
recorded electrical activity data, and correlate the electrical activity data
to the anatomic data.
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The processing unit comprises an algorithm configured to determine the
conduction velocity
of a depolarizing conduction wave at a location correlating to the anatomic
data.
[0025] According to an aspect of the present inventive concepts, a system
for diagnosing
an arrhythmia of a patient comprises: a diagnostic catheter for insertion into
the heart of the
patient, and a processing unit. The diagnostic catheter is configured to
record anatomic and
electrical activity data of the patient. The processing unit is configured to
receive the
recorded electrical activity data, and correlate the electrical activity data
to the anatomic data.
The processing unit comprises an algorithm configured to identify rotational
conduction at a
location correlating to the anatomic data.
[0026] According to an aspect of the present inventive concepts, a system
for diagnosing
an arrhythmia of a patient comprises: a diagnostic catheter for insertion into
the heart of the
patient, and a processing unit. The diagnostic catheter is configured to
record anatomic and
electrical activity data of the patient. The processing unit is configured to
receive the
recorded electrical activity data, and correlate the electrical activity data
to the anatomic data.
The processing unit comprises an algorithm configured to identify irregular
conduction at a
location correlating to the anatomic data.
[0027] According to an aspect of the present inventive concepts, a system
for diagnosing
an arrhythmia of a patient comprises: a diagnostic catheter for insertion into
the heart of the
patient, and a processing unit. The diagnostic catheter is configured to
record anatomic and
electrical activity data of the patient. The processing unit is configured to
receive the
recorded electrical activity data, and correlate the electrical activity data
to the anatomic data.
The processing unit comprises an algorithm configured to identify focal
activation at a
location correlating to the anatomic data.
[0028] According to an aspect of the present inventive concepts, a system
for producing
diagnostic results related to a cardiac condition of a patient, comprises: a
diagnostic catheter
for insertion into the heart of the patient, the diagnostic catheter
configured to record
electrical activity data of the patient at multiple recording locations; and a
processing unit for
receiving the recorded electrical activity data. The system further comprises
an algorithm
configured to perform a complexity assessment using the recorded electrical
activity data and
produce the diagnostic results based on the complexity assessment.
[0029] In some embodiments, the diagnostic results comprise an assessment
of
complexity or an assessment of a variation of complexity over time and/or
space. The
diagnostic results can comprise a variation of complexity over time and space.
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1
[0030] In some embodiments, the complexity assessment comprises a macro-
level
complexity assessment.
[0031] In some embodiments, the complexity assessment represents an
assessment of a
portion of a heart chamber, and the multiple recording locations comprise at
least three
recording locations within a heart chamber, and the system determines
calculated electrical
activity data for at least three vertices on the heart wall, and the
calculation is based on
electrical activity data recorded at the at least three recording locations.
The at least three
recording locations can comprise at least three locations on the heart wall.
The portion of the
heart chamber can comprise no more than 7cm2, no more than 4cm2, and/or no
more than
1cm2 of surface of the heart wall. The at least three recording locations can
comprise at least
one location offset from the heart wall.
[0032] In some embodiments, the complexity assessment represents an
assessment of a
portion of a heart chamber, and the multiple recording locations comprise at
least 24
recording locations within a heart chamber, and the system determines
calculated electrical
activity data for at least 64 vertices on the heart wall, and the calculation
is based on electrical
activity data recorded at the at least 24 recording locations. The at least 24
recording
locations can comprise at least 24 heart wall locations. The at least 24
recording locations
can comprise at least 48 heart wall locations. The at least 24 recording
locations can
comprise at least 48 heart wall locations. The at least 24 recording locations
can comprise at
least 48 locations within the heart chamber. The at least 24 recording
locations can comprise
at least 64 locations within the heart chamber. The at least 64 vertices can
comprise at least
100 vertices. The at least 64 vertices can comprise at least 500 vertices. The
at least 64
vertices can comprise at least 3000 vertices. The at least 64 vertices can
comprise at least
5000 vertices. The portion of the heart chamber can comprise at least 1cm2, at
least 4cm2,
and/or at least 7cm2 of surface of the heart wall. The portion of the heart
chamber can
comprise a portion of an atria of the heart.
[0033] In some embodiments, the system determines calculated electrical
activity data for
multiple vertices on the heart wall, and the calculation is based on
electrical activity data
ff
recorded at the at least three recording locations. The recorded electrical
activity data can
comprise voltage data recorded at multiple locations within a chamber of the
patient's heart,
and the multiple locations can include at least one location offset from the
heart wall. The
recorded electrical activity data can comprise voltage data recorded at
multiple locations
within a chamber of the patient's heart, and the multiple locations can
include at least one
location on the heart wall. The recorded electrical activity data can comprise
voltage data
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recorded at multiple locations within a chamber of the patient's heart, and
the multiple
locations can include at least one location on the heart wall and at least one
location offset
from the heart wall. The processing unit can further comprise a second
algorithm, and the
recorded electrical activity data can comprise recorded voltage data, and the
second algorithm
can be configured to calculate surface charge data and/or dipole density data
for each of the
multiple vertices based on the recorded voltage data, and the complexity
assessment can be
based on the surface charge data and/or the dipole density data. The
processing unit can
further comprise a third algorithm, and the third algorithm can be configured
to convert the
surface charge data and/or dipole density data into surface voltage data, and
the complexity
assessment can be based on the surface voltage data.
[0034] In some embodiments, the complexity assessment is based on
electrical activity
data comprising between 1 and 10 activations.
[0035] In some embodiments, the complexity assessment is based on
electrical activity
data recorded over a time period between 0.3ms and 2000ms. The complexity
assessment
can be based on electrical activity data recorded over a time period of
approximately 150ms.
[0036] In some embodiments, the complexity assessment is based on
electrical activity
data comprising between 3 and 3000 activations. The complexity assessment can
be based
on electrical activity data comprising between 10 and 600 activations. The
complexity
assessment can be based on electrical activity data comprising between 25 and
300
activations.
[0037] In some embodiments, the complexity assessment is based on
electrical activity
data recorded over a time period between 0.3secs and 500secs. The complexity
assessment
can be based on electrical activity data recorded over a time period between
lsec and 90secs.
The complexity assessment can be based on electrical activity data recorded
over a time
period between 4secs and 30secs.
[0038] In some embodiments, the complexity assessment is based on
electrical activity
data comprising between 2,000 and 300,000 activations. The complexity
assessment can be
based on electrical activity data comprising between 6,000 and 40,000
activations.
[0039] In some embodiments, the complexity assessment is based on
electrical activity
data recorded over a time period between 5mins and 8hrs. The complexity
assessment can be
based on electrical activity data recorded over a time period between 15mins
and 50mins.
[0040] In some embodiments, the diagnostic results comprise an assessment
of
complexity at a single heart wall location. The system can further comprise a
display, and the
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system can provide on the display the diagnostic results relative to an image
of the patient's
anatomy.
[0041] In some embodiments, the diagnostic results comprise an assessment
of
complexity at multiple heart wall locations. The system can further comprise a
display, and
the system can provide on the display the diagnostic results relative to an
image of the
patient's anatomy.
[0042] In some embodiments, the diagnostic results comprise an assessment
of
complexity over time. The diagnostic results can comprise an assessment of
complexity over
a pre-determined time duration.
[0043] In some embodiments, the diagnostic catheter comprises at least one
electrode.
[0044] In some embodiments, the diagnostic catheter comprises at least
three electrodes.
[0045] In some embodiments, the diagnostic catheter comprises at least one
ultrasound
transducer.
[0046] In some embodiments, the diagnostic catheter comprises multiple
splines, and
each spline comprises at least one electrode and at least one ultrasound
transducer.
[0047] In some embodiments, the cardiac condition comprises an arrhythmia.
The
cardiac condition can comprise atrial fibrillation.
[0048] In some embodiments, the cardiac condition comprises a condition
selected from
the group consisting of: atrial fibrillation; atrial flutter; atrial
tachycardia; atrial bradycardia,
ventricular tachycardia; ventricular bradycardia; ectopy; congestive heart
failure; angina;
arterial stenosis; and combinations thereof.
[0049] In some embodiments, the cardiac condition comprises a condition
selected from
the group consisting of: heterogeneous activation, conduction, depolarization,
and/or
repolarization that varies in time, space, magnitude, and/or state; irregular
patterns such as
focal, re-entrant, rotational, pivoting, irregular in direction, irregular in
velocity; functional
block; permanent block; and combinations thereof.
[0050] In some embodiments, the system is further configured to collect
additional
patient data, and the complexity assessment is further based on the additional
patient data.
The diagnostic catheter can be configured to record the additional patient
data. The
diagnostic catheter can comprise at least one sensor configured to record the
additional
patient data. The system can comprise at least one sensor configured to record
the additional
patient data. The at least one sensor can be configured to be inserted in the
patient when
recording the additional patient data. The at least one sensor can be
configured to be
positioned external to the patient when recording the additional patient data.
The sensor can
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comprise a sensor selected from the group consisting of: an electrode or other
sensor for
recording electrical activity; a force sensor; a pressure sensor; a magnetic
sensor; a motion
sensor; a velocity sensor; an accelerometer; a strain gauge; a physiologic
sensor; a glucose
sensor; a pH sensor; a blood sensor; a blood gas sensor; a blood pressure
sensor; a flow
sensor; an optical sensor; a spectrometer; an interferometer; a measuring
sensor, such as to
measure size, distance, and/or thickness; a tissue assessment sensor; and
combinations
thereof. The additional patient data can comprise: mechanical information;
physiologic
information, and/or functional information of the patient. The additional
patient data can
comprise data related to a parameter selected from the group consisting of:
heart wall motion;
heart wall velocity; heart tissue strain; magnitude and/or direction of heart
blood flow;
voracity of blood; heart valve mechanics; blood pressure; tissue properties,
such as density,
tissue characteristics and/or biomarkers for tissue characteristics, such as
metabolic activity
or pharmaceutical uptake; tissue composition (e.g. collagen, myocardium, fat,
connective
tissue); and combinations thereof. The complexity assessment can include an
assessment of a
characteristic selected from the group consisting of: electrical-mechanical
delay of tissue;
magnitude ratio of an electrical to a mechanical characteristic; and
combinations thereof.
[0051] In some embodiments, the system is further configured to treat an
arrhythmia, and
the system further comprises an ablation catheter for insertion into the heart
of the patient,
and the ablation catheter is configured to deliver ablation energy to various
locations on the
heart wall. The algorithm can be configured to determine at least one ablation
location, the at
least one ablation location can comprise one or more heart wall locations for
receiving the
ablation energy from the ablation catheter, the at least one ablation location
can be
determined based on the complexity assessment and/or the diagnostic results.
The at least
one ablation location can comprise one or more heart locations where
complexity exceeds a
threshold. The at least one ablation location can comprise a location of
highest complexity in
a region of multiple determined complexities. The ablation catheter can be
configured to
deliver one or more ablation energies selected from the group consisting of:
electromagnetic
energy; RF energy; microwave energy; thermal energy; heat energy; cryogenic
energy; light
energy; laser light energy; chemical energy; sound energy; ultrasound energy;
mechanical
energy; and combinations thereof. The system can further comprise an energy
delivery unit
configured to provide the ablation energy to the ablation catheter. The energy
delivery unit
can be configured to deliver one or more ablation energies selected from the
group consisting
of: electromagnetic energy; RF energy; microwave energy; thermal energy; heat
energy;
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cryogenic energy; light energy; laser light energy; chemical energy; sound
energy; ultrasound
energy; and combinations thereof.
[0052] The technology described herein, along with the attributes and
attendant
advantages thereof, will best be appreciated and understood in view of the
following detailed
description taken in conjunction with the accompanying drawings in which
representative
embodiments are described by way of example.
BRIEF DESCRIPTION OF THE DRAWINGS
[0053] Fig. 1 illustrates a block diagram of a cardiac information
processing system,
consistent with the present inventive concepts.
[0054] Fig. 2A illustrates a visual representation of a data structure of a
cardiac
information processing system, consistent with the present inventive concepts.
[0055] Fig. 2B illustrates a visual representation of a portion of a data
structure of a
cardiac information processing system, consistent with the present inventive
concepts.
[0056] Fig. 3 illustrates a schematic view of an algorithm for performing a
complexity
assessment, consistent with the present inventive concepts.
[0057] Fig. 3A illustrates a schematic view of an algorithm for performing
a complexity
assessment, consistent with the present inventive concepts.
[0058] Fig. 4 illustrates a schematic view of an algorithm for determining
conduction
velocity data, consistent with the present inventive concepts.
[0059] Fig. 5 illustrates a schematic view of an algorithm for determining
localized
rotational activity, consistent with the present inventive concepts.
[0060] Fig. 5A illustrates a graphical representation of anatomic data
including a
neighborhood of vertices defined by an outer ring of vertices, consistent with
the present
inventive concepts.
[0061] Fig. 5B illustrates a simplified representation of a neighborhood
including an
outer ring of vertices positioned about a central vertex, consistent with the
present inventive
concepts.
[0062] Fig. 5C illustrates a representative anatomy showing a propagating
wave rotating
about a neighborhood, consistent with the present inventive concepts.
[0063] Fig. 5D illustrates a plot of activation times in the outer ring of
vertices of Fig.
5C, consistent with the present inventive concepts.
[0064] Fig. 5E illustrates a graph of conduction velocity vectors of Fig.
5C, consistent
with the present inventive concepts.
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[0065] Fig. 6 illustrates a schematic view of an algorithm for determining
localized
irregular activity, consistent with the present inventive concepts.
[0066] Fig. 6A illustrates an example of a propagation wave showing
irregular activity,
consistent with the present inventive concepts.
[0067] Fig. 7 illustrates a schematic view of an algorithm for determining
focal
activation, consistent with the present inventive concepts.
[0068] Figs. 7A and 7B illustrate a representative anatomy showing focal
activation,
consistent with the present inventive concepts.
[0069] Fig. 8 illustrates a display on which cardiac data can be rendered,
consistent with
the present inventive concepts.
[0070] Figs. 9 and 9A illustrate a schematic view of a mapping catheter,
and a
perspective anatomic view of a heart chamber with a mapping catheter inserted
into the
chamber, consistent with the present inventive concepts
DETAILED DESCRIPTION OF THE DRAWINGS
[0071] Reference will now be made in detail to the present embodiments of
the
technology, examples of which are illustrated in the accompanying drawings.
Similar
reference numbers may be used to refer to similar components. However, the
description is
not intended to limit the present disclosure to particular embodiments, and it
should be
construed as including various modifications, equivalents, and/or alternatives
of the
embodiments described herein.
[0072] It will be understood that the words "comprising" (and any form of
comprising,
such as "comprise" and "comprises"), "having" (and any form of having, such as
"have" and
"has"), "including" (and any form of including, such as "includes" and
"include") or
"containing" (and any form of containing, such as "contains" and "contain")
when used
herein, specify the presence of stated features, integers, steps, operations,
elements, and/or
components, but do not preclude the presence or addition of one or more other
features,
integers, steps, operations, elements, components, and/or groups thereof.
[0073] It will be further understood that, although the terms first,
second, third, etc. may
be used herein to describe various limitations, elements, components, regions,
layers and/or
sections, these limitations, elements, components, regions, layers and/or
sections should not
be limited by these terms. These terms are only used to distinguish one
limitation, element,
component, region, layer or section from another limitation, element,
component, region,
layer or section. Thus, a first limitation, element, component, region, layer
or section
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discussed below could be termed a second limitation, element, component,
region, layer or
section without departing from the teachings of the present application.
[0074] It will be further understood that when an element is referred to as
being "on",
"attached", "connected" or "coupled" to another element, it can be directly on
or above,
or connected or coupled to, the other element, or one or more intervening
elements can be
present. In contrast, when an element is referred to as being "directly on",
"directly
attached", "directly connected" or "directly coupled" to another element,
there are no
intervening elements present. Other words used to describe the relationship
between
elements should be interpreted in a like fashion (e.g. "between" versus
"directly between,"
"adjacent" versus "directly adjacent," etc.).
[0075] It will be further understood that when a first element is referred
to as being "in",
"on" and/or "within" a second element, the first element can be positioned:
within an
internal space of the second element, within a portion of the second element
(e.g. within a
wall of the second element); positioned on an external and/or internal surface
of the second
element; and combinations of one or more of these.
[0076] As used herein, the term "proximate", when used to describe
proximity of a first
component or location to a second component or location, is to be taken to
include one or
more locations near to the second component or location, as well as locations
in, on and/or
within the second component or location. For example, a component positioned
proximate
an anatomical site (e.g. a target tissue location), shall include components
positioned near to
the anatomical site, as well as components positioned in, on and/or within the
anatomical site.
[0077] Spatially relative terms, such as "beneath," "below," "lower,"
"above,"
"upper" and the like may be used to describe an element and/or feature's
relationship to
another element(s) and/or feature(s) as, for example, illustrated in the
figures. It will be
further understood that the spatially relative terms are intended to encompass
different
orientations of the device in use and/or operation in addition to the
orientation depicted in the
figures. For example, if the device in a figure is turned over, elements
described as "below"
and/or "beneath" other elements or features would then be oriented "above" the
other
elements or features. The device can be otherwise oriented (e.g. rotated 90
degrees or at
other orientations) and the spatially relative descriptors used herein
interpreted accordingly.
[0078] The terms "reduce", "reducing", "reduction" and the like, where used
herein,
are to include a reduction in a quantity, including a reduction to zero.
Reducing the
likelihood of an occurrence shall include prevention of the occurrence.
Correspondingly, the
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terms "prevent", "preventing", and "prevention" shall include the acts of
"reduce",
"reducing", and "reduction", respectively.
[0079] The term "and/or" where used herein is to be taken as specific
disclosure of each
of the two specified features or components with or without the other. For
example, "A
and/or B" is to be taken as specific disclosure of each of (i) A, (ii) B and
(iii) A and B, just as
if each is set out individually herein.
[0080] In this specification, unless explicitly stated otherwise, "and" can
mean "or,"
and "or" can mean "and." For example, if a feature is described as having A,
B, or C, the
feature can have A, B, and C, or any combination of A, B, and C. Similarly, if
a feature is
described as having A, B, and C, the feature can have only one or two of A, B,
or C.
[0081] The expression "configured (or set) to" used in the present
disclosure may be
used interchangeably with, for example, the expressions "suitable for",
"having the capacity
to", "designed to", "adapted to", "made to" and "capable of' according to a
situation. The
expression "configured (or set) to" does not mean only "specifically designed
to" in
hardware. Alternatively, in some situations, the expression "a device
configured to" may
mean that the device "can" operate together with another device or component.
[0082] As used herein, the term "threshold" refers to a maximum level, a
minimum
level, and/or range of values correlating to a desired or undesired state. In
some
embodiments, a system parameter is maintained above a minimum threshold, below
a
maximum threshold, within a threshold range of values and/or outside a
threshold range of
values, to cause a desired effect (e.g. efficacious therapy) and/or to prevent
or otherwise
reduce (hereinafter "prevent") an undesired event (e.g. a device and/or
clinical adverse
event). In some embodiments, a system parameter is maintained above a first
threshold (e.g.
above a first temperature threshold to cause a desired therapeutic effect to
tissue) and below a
second threshold (e.g. below a second temperature threshold to prevent
undesired tissue
damage). In some embodiments, a threshold value is determined to include a
safety margin,
such as to account for patient variability, system variability, tolerances,
and the like. As used
herein, "exceeding a threshold" relates to a parameter going above a maximum
threshold,
below a minimum threshold, within a range of threshold values and/or outside
of a range of
threshold values. Thresholds can be defined by a user (e.g. a clinician of the
patient), and/or
system defined (e.g. in manufacturing of the system).
[0083] The term "diameter" where used herein to describe a non-circular
geometry is to
be taken as the diameter of a hypothetical circle approximating the geometry
being described.
For example, when describing a cross section, such as the cross section of a
component, the
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term "diameter" shall be taken to represent the diameter of a hypothetical
circle with the
same cross-sectional area as the cross section of the component being
described.
[0084] The terms "major axis" and "minor axis" of a component where used
herein are
the length and diameter, respectively, of the smallest volume hypothetical
cylinder which can
completely surround the component.
[0085] As used herein, the term "functional element" is to be taken to
include one or
more elements constructed and arranged to perform a function. A functional
element can
comprise a sensor and/or a transducer. In some embodiments, a functional
element is
configured to deliver energy and/or otherwise treat tissue (e.g. a functional
element
configured as a treatment element). Alternatively or additionally, a
functional element (e.g. a
functional element comprising a sensor) can be configured to record one or
more parameters,
such as a patient physiologic parameter; a patient anatomical parameter (e.g.
a tissue
geometry parameter); a patient environment parameter; and/or a system
parameter. In some
embodiments, a sensor or other functional element is configured to perform a
diagnostic
function (e.g. to record data used to perform a diagnosis). In some
embodiments, a functional
element is configured to perform a therapeutic function (e.g. to deliver
therapeutic energy
and/or a therapeutic agent). In some embodiments, a functional element
comprises one or
more elements constructed and arranged to perform a function selected from the
group
consisting of: deliver energy; extract energy (e.g. to cool a component);
deliver a drug or
other agent; manipulate a system component or patient tissue; record or
otherwise sense a
parameter such as a patient physiologic parameter or a system parameter; and
combinations
of one or more of these. A functional element can comprise a fluid and/or a
fluid delivery
system. A functional element can comprise a reservoir, such as an expandable
balloon or
other fluid-maintaining reservoir. A "functional assembly" can comprise an
assembly
constructed and arranged to perform a function, such as a diagnostic and/or
therapeutic
function. A functional assembly can comprise an expandable assembly. A
functional
assembly can comprise one or more functional elements.
[0086] The term "transducer" where used herein is to be taken to include
any
component or combination of components that receives energy or any input, and
produces an
output. For example, a transducer can include an electrode that receives
electrical energy,
and distributes the electrical energy to tissue (e.g. based on the size of the
electrode). In some
configurations, a transducer converts an electrical signal into any output,
such as light (e.g. a
transducer comprising a light emitting diode or light bulb), sound (e.g. a
transducer
comprising a piezo crystal configured to deliver ultrasound energy), pressure,
heat energy,
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cryogenic energy, chemical energy; mechanical energy (e.g. a transducer
comprising a motor
or a solenoid), magnetic energy, and/or a different electrical signal (e.g. a
Bluetooth or other
wireless communication element). Alternatively or additionally, a transducer
can convert a
physical quantity (e.g. variations in a physical quantity) into art electrical
signal. A
transducer can include any component that delivers energy and/or an agent to
tissue, such as a
transducer configured to deliver one or more of: electrical energy to tissue
(e.g. a transducer
comprising one or more electrodes); light energy to tissue (e.g. a transducer
comprising a
laser, light emitting diode and/or optical component such as a lens or prism);
mechanical
energy to tissue (e.g. a transducer comprising a tissue manipulating element);
sound energy to
tissue (e.g. a transducer comprising a piezo crystal); chemical energy;
electromagnetic
energy; magnetic energy; and combinations of one or more of these.
[0087] As used herein, the term "fluid" can refer to a liquid, gas, gel, or
any flowable
material, such as a material which can be propelled through a lumen and/or
opening.
[0088] It is appreciated that certain features of the invention, which are,
for clarity,
described in the context of separate embodiments, may also be provided in
combination in a
single embodiment. Conversely, various features of the invention which are,
for brevity,
described in the context of a single embodiment, may also be provided
separately or in any
suitable sub-combination. For example, it will be appreciated that all
features set out in any
of the claims (whether independent or dependent) can be combined in any given
way.
[0089] It is to be understood that at least some of the figures and
descriptions of the
invention have been simplified to focus on elements that are relevant for a
clear
understanding of the invention, while eliminating, for purposes of clarity,
other elements that
those of ordinary skill in the art will appreciate may also comprise a portion
of the invention.
However, because such elements are well known in the art, and because they do
not
necessarily facilitate a better understanding of the invention, a description
of such elements is
not provided herein.
[0090] Terms defined in the present disclosure are only used for describing
specific
embodiments of the present disclosure and are not intended to limit the scope
of the present
disclosure. Terms provided in singular forms are intended to include plural
forms as well,
unless the context clearly indicates otherwise. All of the terms used herein,
including
technical or scientific terms, have the same meanings as those generally
understood by an
ordinary person skilled in the related art, unless otherwise defined herein.
Terms defined in a
generally used dictionary should be interpreted as having meanings that are
the same as or
similar to the contextual meanings of the relevant technology and should not
be interpreted as
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having ideal or exaggerated meanings, unless expressly so defined herein. In
some cases,
terms defined in the present disclosure should not be interpreted to exclude
the embodiments
of the present disclosure.
[0091] Provided herein are cardiac information systems for producing
diagnostic results
related to a cardiac condition of a patient. The systems can be used to
perform a medical
procedure on a patient, such as a diagnostic, prognostic, and/or therapeutic
procedure on the
patient. The systems can identify cardiac conduction patterns of a patient,
such as an
arrhythmia patient. The system includes a diagnostic catheter for insertion
into the heart of
the patient. The diagnostic catheter can be configured to record electrical
activity data of the
patient, such as when the catheter includes one or more electrodes for
measuring voltage.
The system can fluffier include a processing unit for receiving the recorded
electrical activity
data. The processing unit can comprise an algorithm configured to perform one
or more
functions, such as to produce calculated electrical activity data, complexity
assessment,
and/or the diagnostic results. In some embodiments, the algorithm performs a
complexity
assessment to produce the diagnostic results. In some embodiments, the
complexity
assessment is performed by one or more algorithms described herein, which
solely or in
combination with another algorithm perform a complexity assessment. In some
embodiments, the system further includes a treatment device, such as a cardiac
ablation
device and/or a pharmaceutical agent.
[0092] Referring now to Fig. 1, a block diagram of an embodiment of a
cardiac
information processing system is illustrated, consistent with the present
inventive concepts.
The cardiac information processing system, system 100 shown, can be or include
a system
configured to perform cardiac mapping, diagnosis, prognosis, and/or treatment,
such as for
treating a disease or disorder of a patient, such as an arrhythmia or other
cardiac condition as
described herein. Additionally or alternatively, system 100 can be a system
configured for
teaching and or validating devices and methods of diagnosing and/or treating
cardiac
abnormalities or disease of a patient P. System 100 can further be used for
generating
displays of cardiac activity, such as dynamic displays of active wave fronts
propagating
across surfaces of the heart. In some embodiments, system 100 produces
diagnostic results
1100. Diagnostic results 1100 represent diagnostic data related to a cardiac
condition of a
patient, such as diagnostic results based on a complexity assessment as
described herein.
[0093] System 100 includes a catheter 10, a cardiac information console 20,
and a patient
interface module 50 that can be configured to cooperate (e.g. collectively
cooperate) to
accomplish the various functions of the system 100. System 100 can include a
single power
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supply (PWR), which can be shared by console 20 and the patient interface
module 50. Use
of a single power supply in this way can greatly reduce the chance for leakage
cunents to
propagate into the patient interface module 50 and cause errors in
localization (e.g. the
process of determining the location of one or more electrodes within the body
of patient P).
Console 20 includes bus 21 which electrically and/or otherwise operatively
connects various
components of console 20 to each other, as shown in Fig. 1.
[0094] Catheter 10 includes an electrode array 12 that can be
percutaneously delivered to
a heart chamber (HC). In this embodiment, the array of electrodes 12 has a
known spatial
configuration in three-dimensional (3D) space. For example, in an expanded
state the
physical relationship of the electrode array 12 can be known or reliably
assumed. Electrode
array 12 can include at least one electrode 12a, or at least three electrodes
12a. Diagnostic
catheter 10 also includes a handle 14, and an elongate flexible shaft 16
extending from handle
14. Attached to a distal end of shaft 16 is the electrode array 12, such as a
radially
expandable and/or compactable assembly. In this embodiment, the electrode
array 12 is
shown as a basket array, but the electrode array 12 could take other forms in
other
embodiments. In some embodiments, expandable electrode array 12 can be
constructed and
arranged as described in reference to applicant's International PCT Patent
Application Serial
Number PCT/U52013/057579, titled "SYSTEM AND METHOD FOR DIAGNOSING AND
TREATING HEART TISSUE," filed August 30, 2013, and International PCT Patent
Application Serial Number PCT/US2014/015261, titled "EXPANDABLE CATHETER
ASSEMBLY WITH FLEXIBLE PRINTED CIRCUIT BOARD," filed February 7,2014, the
content of each of which is incorporated herein by reference in its entirety
for all purposes.
In other embodiments, expandable electrode array 12 can comprise a balloon,
radially
deployable arms, spiral array, and/or other expandable and compactible
structure (e.g. a
resiliently biased structure).
[0095] Shaft 16 and expandable electrode array 12 are constructed and
arranged to be
inserted into a body (e.g. an animal body or a human body, such as the body of
Patient P),
and advanced through a body vessel, such as a femoral vein and/or other blood
vessel. Shaft
16 and electrode array 12 can be constructed and arranged to be inserted
through an
introducer (not shown, but such as a transseptal sheath), such as when
electrode array 12 is in
a compacted state, and slidingly advanced through a lumen of the introducer
into a body
space, such as a chamber of the heart (HC), such as the right atrium or the
left atrium, as
examples.
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[0096] Expandable electrode array 12 can comprise multiple splines (e.g.
multiple splines
resiliently biased in the basket shape shown in Fig. 1), each spline having a
plurality of
electrodes 12a and/or a plurality of ultrasound (US) transducers 12b. Three
splines are
visible in FIG. 1, but the basket array is not limited to three splines, more
or less splines can
be included in the basket array. Each electrode 12a can be configured to
record (e.g. record,
measure, and/or sense, herein) a bio-potential (also referred to as
"electrical activity" herein),
such as the voltage level at a location on a surface of the heart and/or at a
location within a
heart chamber HC. Recorded electrical activity is stored by system 100 as
electrical activity
data 120a. System 100 can perform one or more calculations on the recorded
electrical
activity data 120a to produce calculated electrical activity data 120b.
Electrical activity data
Li
120 can comprise recorded electrical activity data 120a and/or calculated
electrical activity
data 120b. Calculated electrical activity data 120b can comprise data selected
from the group
consisting of: voltage data; mathematically processed voltage data (e.g. data
that is averaged,
integrated, sorted, had minimum and/or maximum values determined, and/or
otherwise is
mathematically processed); surface charge data; dipole density data; timing
data of electrical
events; filtered electrical data; electrical pattern and/or template data; an
image formed by
electrical values at multiple locations; and combinations of one, two, or more
of these. As
used herein, the term dipole density, surface charge, and surface charge
density, shall be used
interchangeably.
[0097] Calculated electrical activity data 120b can comprise data that
represents instances
of electrical activation (also referred to as "activation" herein) of heart
tissue, activation
timing data 121. In some embodiments, calculated electrical activity data 120b
comprises
data that represents, conduction velocity, conduction velocity data 122,
and/or conduction
divergence, conduction divergence data 123, each described herebelow.
Calculated electrical
activity data 120b can be correlated to one or more locations of the heart,
referred to as a
vertex (single location) and vertices (multiple locations) herein. In some
embodiments,
calculated electrical activity data comprises data selected from the group
consisting of:
electrical differences (e.g. deltas); averages; weighted averages; patterns
and/or templates;
degree-of-fit (e.g. best-fit) to one or more patterns or templates; "flow"
between two or more
images formed by electrical values at multiple locations (e.g. as calculated
by one, two, or
more optical flow algorithms, such as Horn-Schunek and/or a Lucas-Kanade
algorithm); data
analytics and/or statistics techniques, such as classification or
categorization, of electrical
activity using a training data set (e.g. separately acquired data, such as
historical data); a
computationally-optimized fit (e.g. machine learning or predictive analysis,
such as by neural
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network or deep learning, cluster analysis); and combinations of one, two, or
more of these.
The calculated electrical activity data can comprise a probabilistic model
that uses one or
more of the aforementioned methods as inputs.
[0098] In some embodiments, activation is determined by an algorithm (e.g.
an activation
detection algorithm) which can include: comparing electrical data to a
threshold;
measurement of the slope and/or maximum and/or minimum of the electrical data;
comparing
electrical data at one location to electrical data at one or more nearby
locations (e.g. weighted
comparison); and combinations of these. In some embodiments, the activation
detection
algorithm can be of similar construction and arrangement as described in
reference to
applicant's International PCT Patent Application Serial Number
PCT/US2017/030915, titled
"CARDIAC INFORMATION DYNAMIC DISPLAY SYSTEM AND METHOD", filed
May 3, 2017, and International PCT Patent Application Serial Number
PCT/U52017/030922,
titled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY ALGORITHM", filed May 3,
2017, the content of each of which is incorporated herein by reference in its
entirety for all
purposes. To promote the spatial continuity for a propagation history map, the
activation
detection algorithm can comprise two parallel lines considering both raw
signal (e.g. dipole
density data and/or voltage data) together with a spatial Laplacian signal. In
some
embodiments, the activation detection algorithm further includes conduction
velocity as one
consideration of selecting between potential active timings, as well as
developing voting
schemes on multiple features, such as gradient, spatial Laplacian, peak
amplitude, and/or
other such features.
[0099] Expanding upon the conduction velocity addition to the activation
detection, the
problem can be represented as a cost function with either regularization on
the conduction
velocity or as an inequality constraint on the conduction velocity. In some
embodiments, the
activation detection algorithm creates a Gaussian probability distribution
function around
each detected activation where the highest probability is at the currently
detected activation.
Given no constraints, maximizing the probability of activation for every
channel can output a
propagation history. Alternatively, including at least one constraint can
limit the solution to
comprise a physiologically reasonable conduction (e.g., less than 2m/s) and
can be
configured to shift the activations slightly from the currently selected
activation times. Below
shows an example of how the cost function can be written with constrained
conduction
velocity:
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# of Vert
max( PU, TO), s. t. Conduction Velocityi < Constant (1)
[0100] where P is the probability of activation occurring at a particular
vertex, i, at time,
T. The conduction velocity calculation is dependent on T.
[0101] In some embodiments, the activation detection algorithm comprises a
local
minimum of temporal derivative of unipolar electro gram with a minimum
separation between
activations set to a time threshold (e.g. between 50-150ms)
[0102] In some embodiments, the activation detection algorithm comprises a
local
minimum or maximum of bipolar or Laplacian electrograms with a minimum
separation
between activations set to a time threshold (e.g. between 50-150ms)
[0103] In some embodiments, the activation detection algorithm comprises
standard
filtering with a bandpass of (0.5 to 1Hz)-(100-300 Hz), or after an aggressive
band pass of
(10-30Hz)-(100-300 Hz).
[0104] In some embodiments, the activation detection algorithm comprises a
local
minimum and/or maximum of temporal derivative of bipolar electrograms or
Laplacian
electrograms with a minimum separation between activations set to a time
threshold (e.g.
between 50-150m5). The activation detection algorithm can further comprise
standard
filtering with a bandpass of (0.5 to 1Hz)-(100-300 Hz) or after aggressive
band pass of (10-
30Hz)-(100-300 Hz).
[0105] In some embodiments, the activation detection algorithm comprises
zero crossings
of Laplacian electrograms after a negative deflection with a minimum
separation between
activations set to a time threshold (e.g. between 50-150ms).
[0106] In some embodiments, the activation detection algorithm comprises
local
maximums of Hilbert transformed electrograms (Phase Mapping) with a minimum
separation
between activations set to a time threshold (e.g. between 50-150ms).
[0107] In some embodiments, the activation detection algorithm can comprise
an
algorithm expressed as a supervised learning problem utilizing machine
learning (e.g. neural
networks, support vector machines, and/or deep learning). In these
embodiments, the
algorithm can use a training data set, such as a data set including historic
data and/or
simulated data.
[0108] Each US transducer 12b can be configured to transmit an ultrasound
signal and
receive ultrasound reflections to determine the range to a reflecting target
such as at a point
on the surface of a heart chamber (HC), to provide anatomic data used in a
digital model
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creation of the anatomy. Recorded ultrasound data and/or other anatomic data
can be stored
by system 100 as anatomic data 110. Electrical activity data 120 (e.g.
including activation
timing data 121, conduction velocity data 122, and/or conduction divergence
data 123) and/or
anatomic data 110 can be stored in memory of system 100, for example storage
device 25
described herebelow.
[0109] As a non-limiting example, three electrodes 12a and three US
transducers 12b are
shown on each spline in this embodiment. However, in other embodiments, the
basket array
can include more or less electrodes and/or more or less US transducers.
Furthermore, the
electrodes 12a and transducers 12b can be arranged in pairs. Here, one
electrode 12a is
paired with one transducer 12b, with multiple electrode-transducer pairs per
spline. The
inventive concept is not, however, limited to this particular electrode-
transducer arrangement.
In other embodiments, not all electrodes 12a and transducers 12b need to be
arranged in
pairs, some could be arranged in pairs while others are not arranged in pairs.
Also, in some
embodiments, not all splines comprise the same arrangement of electrodes 12a
and
transducers 12b. Additionally, in some embodiments, electrodes 12a are
arranged on a first
set of splines, while transducers 12b are arranged on a second set of splines.
Array 12 can
comprise at least four electrodes 12a, such as at least 24 electrodes 12a,
such as at least 48
electrodes. Array 12 can comprise at least three splines, such as at least
four splines, such as
at least six splines.
[0110] In some embodiments, a second catheter, catheter 10', is used in
conjunction with
catheter 10, for example a basket or other array of electrodes of catheter 10'
can be
positioned in a separate heart chamber to simultaneously map more than one
chamber of the
heart. Catheter 10' can be of similar or dissimilar construction to catheter
10, described
herein. The electrode array of catheter 10' can be arranged in a different
configuration than
the electrode array 12 of catheter 10. For example, the array of catheter 10'
can only have 24
electrodes and no US transducers while array 12 of catheter 10 possesses 48
electrodes and
48 US transducers. Catheter 10 and/or 10' can comprise two or more electrode
arrays, such
as array 12 shown, and a second array, positioned proximal to array 12 (e.g.
on shaft 16 of
catheter 10 or 10').
[0111] Catheter 10 can comprise a cable or other conduit, such as cable 18,
configured to
electrically, optically, and/or electro-optically connect catheter 10 to
console 20 via
connectors 18a and 20a, respectively. In some embodiments, cable 18 comprises
a
mechanism selected from the group consisting of: a cable such as a steering
cable; a
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mechanical linkage; a hydraulic tube; a pneumatic tube; and combinations of
one or more of
these.
[0112] Patient interface module 50 can be configured to electrically
isolate one or more
components of console 20 from patient P (e.g. to prevent undesired delivery of
a shock or
other undesired electrical energy to patient P). The patient interface module
50 can be
integral with console 20 and/or it can comprise a separate discrete component
(e.g. separate
housing), as is shown. Console 20 comprises one or more connectors 20b, each
comprising a
jack, plug, terminal, port, or other custom or standard electrical, optical,
and/or mechanical
connector. In some embodiments, the connectors 20b are terminated to maintain
desirable
input impedance over RF frequencies, such as 10 kilohertz to 20 megahertz. In
some
embodiments, the termination is achieved by terminating the cable shield with
a filter. In
some embodiments, the terminating filters provide high input impedance in one
frequency
range, for example to minimize leakage at localization frequencies, and low
input impedance
in a different frequency range, for example to achieve maximum signal
integrity at ultrasound
frequencies. Similarly, the patient interface module 50 includes one or more
connectors 50b.
At least one cable 52 connects the patient interface module 50 with console
20, via
connectors 20b and 50b.
[0113] In this embodiment, the patient interface module 50 includes an
isolated
localization drive system 54, a set of patch electrodes 56, and one or more
reference
electrodes 58. The isolated localization drive system 54 isolates localization
signals from the
rest of system 100 to prevent current leakage (e.g. signal loss) resulting in
performance
degradation. In some embodiments, the isolation of the localization signals
from the
remainder of the system comprises a range of impedance greater than 100
kiloohms, such as
approximately 500 kiloohms at the localization frequencies. The isolation of
the localization
drive system 54 can minimize drift in localization positions and maintain a
high degree of
isolation between axes (as described herebelow). The localization drive system
54 can
operate as a current, voltage, magnetic, acoustic, or other type of energy
modality drive. The
set of patch electrodes 56 and/or one or more reference electrodes 58 can
consist of
conductive electrodes, magnetic coils, acoustic transducers, and/or other type
of transducer or
sensor based on the energy modality employed by the localization drive system
54.
Additionally, the isolated localization drive system 54 maintains simultaneous
output on all
axes (e.g. a localization signal is present on each axis electrode pair, while
also increasing the
effective sampling rate at each electrode position). In some embodiments, the
localization
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sampling rate comprises a rate between 10kHz and 20MHz, such as a sampling
rate of
approximately 6251(Hz.
[0114] In some embodiments, the set of patch electrodes 56 include three
(3) pairs of
patch electrodes: an "X" pair having two patch electrodes placed on opposite
sides of the ribs
(Xl, X2); a "Y" pair having one patch electrode placed on the lower back (Y1)
and one patch
electrode placed on the upper chest (Y2); and a "Z" pair having one patch
electrode placed on
the upper back (Z1) and one patch electrode placed on the lower abdomen (Z2).
The patch
electrode 56 pairs can be placed on any orthogonal and/or non-orthogonal sets
of axes. In the
embodiment of FIG. 1, the placement of electrodes is shown on patient P, where
electrodes
on the back are shown in dashed lines.
[0115] The reference patch electrode 58 can be placed on the lower back /
buttocks.
Additionally, or alternatively, a reference catheter can be placed within a
body vessel, such as
a blood vessel in and/or proximate the lower back / buttocks.
[0116] The placement of electrodes 56 defines a coordinate system made up
of three
axes, one axis per pair of patch electrodes 56. In some embodiments, the axes
are non-
orthogonal to a natural axis of the body, i.e., non-orthogonal to head-to-toe,
chest-to-back,
and side-to-side (e.g. rib-to-rib). The electrodes can be placed such that the
axes intersect at
an origin, such as an origin located in the heart. For instance, the origin of
the three
intersecting axes can be centered in an atrial volume. System 100 can be
configured to
provide an "electrical zero" that is positioned outside of the heart, such as
by locating a
reference electrode 58 such that the resultant electrical zero is outside of
the heart (e.g. to
avoid crossing from a positive voltage to a negative voltage at one or more
locations being
localized).
[0117] As described above, a patch pair can operate differentially, such as
when neither
patch 56 in a pair operates as a reference electrode, and are both driven by
system 100 to
generate the electrical field between the two. Alternatively or additionally,
one or more of
the patch electrodes 56 can serve as the reference electrode 58, such that
they operate in a
single ended mode. One of any pair of patch electrodes 56 can serve as the
reference
electrode 58 for that patch pair, forming a single-ended patch pair. One or
more patch pairs
can be configured to be independently single-ended. One or more of the patch
pairs can
share a patch as a single-ended reference or can have the reference patches of
more than one
patch pair electrically connected.
[0118] Through processing performed by console 20, the axes can be
transformed (e.g.
rotated) from a first orientation (e.g. a non-physiological orientation based
on the placement
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of electrodes 56) to a second orientation. The second orientation can comprise
a standard
Left-Posterior-Superior (LPS) anatomical orientation, such as when the "x"
axis is oriented
from right to left of the patient, the "y" axis is oriented from the anterior
to posterior of the
patient, and the "z" axis is oriented from caudal to cranial of the patient.
Placement of patch
electrodes 56 and the non-standard axes defined thereby can be selected to
provide improved
spatial resolution when compared to patch electrode placement resulting in a
normal
physiological orientation of the resulting axes (e.g. due to preferred tissue
characteristics
F
between electrodes 56 in the non-standard orientation). For example, non-
standard electrode
56 placement can result in reducing the negative effects of the low-impedance
volume of the
lungs on the localization field. Furthermore, electrode 56 placement can be
selected to create
axes which pass through the body of the patient along paths of equivalent, or
at least similar,
lengths. Axes of similar length will possess more similar energy density per
unit distance
within the body, yielding a more uniform spatial resolution along such axes.
Transforming
the non-standard axes into a standard orientation can provide a more
straightforward display
environment for the user. Once the desired rotation is achieved, each axis can
be scaled, such
as when made longer or shorter, as needed. The rotation and scaling are
performed based on
comparing pre-determined (e.g. expected or known) electrode array 12 shape and
relative
dimensions, with measured values that correspond to the shape and relative
dimensions of the
electrode array in the patch electrode established coordinate system. For
example, rotation
and scaling can be performed to transform a relatively inaccurate (e.g.
uncalibrated)
representation into a more accurate representation. Shaping and scaling the
representation of
the electrode array 12 can adjust, align, and/or otherwise improve the
orientation and relative
sizes of the axes for far more accurate localization.
[0119] The electrical reference electrode(s) 58 can be or at least include
a patch electrode
and/or an electrical reference catheter, which can function as a patient
"analog ground"
reference. A patch electrode 58 can be placed on the skin, and can act as a
return for current
for defibrillation (e.g. provide a secondary purpose). An electrical reference
catheter can
include a unipolar reference electrode used to enhance common mode rejection.
The unipolar
reference electrode, or other electrodes on a reference catheter, can be used
to measure, track,
correct, and/or calibrate physiological, mechanical, electrical, and/or
computational artifacts
in a cardiac signal. In some embodiments, these artifacts are due to
respiration, cardiac
motion, and/or artifacts induced by applied signal processing, such as
filters. Another form
of an electrical reference catheter can be an internal analog reference
electrode, which can act
as a low noise "analog ground" for all internal catheter electrodes. Each of
these types of
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reference electrodes can be placed in relatively similar locations, such as
near the lower back
in an internal blood vessel (as a catheter) and/or on the lower back (as a
patch). In some
embodiments, system 100 comprises a reference catheter 58 including a fixation
mechanism
(e.g. a user activated fixation mechanism), which can be constructed and
arranged to reduce
displacement (e.g. accidental or otherwise unintended movement) of one or more
electrodes
of the reference catheter 58. The fixation mechanism can comprise a mechanism
selected
from the group consisting of: spiral expander; spherical expander;
circumferential expander;
axially actuated expander; rotationally actuated expander; and combinations of
two or more
of these.
[0120] In some embodiments, console 20 includes a defibrillation (DFIB)
protection
module 22 connected to connector 20a, which is configured to receive cardiac
information
from the catheter 10. The DFIB protection module 22 is configured to have a
precise
clamping voltage and a reduced (e.g. minimum) capacitance. Functionally, the
DFIB
protection module 22 acts a surge protector, configured to protect the
circuitry of console 20
during application of high energy to the patient, such as during
defibrillation of the patient
(e.g. using a standard defibrillation device).
[0121] The DFIB protection module 22 can be coupled to three signal paths,
a bio-
potential (BIO) signal path 30, a localization (LOC) signal path 40, and an
ultrasound (US)
signal path 60. Generally, the BIO signal path 30 filters noise and preserves
the recorded bio-
potential data, and also enables the bio-potential signals to be read (e.g.
successfully
recorded) while ablating (e.g. delivery of RF energy to tissue), which is not
the case in other
systems. Generally, the LOC signal path 40 allows high voltage inputs, while
filtering noise
from received localization data. Generally, the US signal path 60 acquires
range data from
the physical structure of the anatomy using the ultrasound transducers 12b for
generation of a
2D or 3D digital model of the heart chamber HC, which can be stored in memory.
[0122] The BIO signal path 30 includes an RF filter 31 coupled to the DFIB
protection
module 22. In this embodiment, the RF filter 31 operates as a low-pass filter
having a high
input impedance. The high input impedance is preferred in this embodiment
because it
minimizes the loss of voltage from the source (e.g. catheter 10), thereby
better preserving the
received signals (e.g. during RF ablation). The RF filter 31 is configured to
allow bio-
potential signals from the electrodes 12a on catheter 10 to pass through RF
filter 31 (e.g.
passing frequencies less than 500Hz), such as frequencies in the range of
0.5Hz to 500Hz.
However, high frequencies, such as high voltage signals used in RF ablation,
are filtered out
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from the bio-potential signal path 30. RF filter 31 can comprise a corner
frequency between
10kHz and 50k1-lz.
[0123] A BIO amplifier 32 can comprise a low noise single-ended input
amplifier that
amplifies the RF filtered signal. A BIO filter 33 (e.g. a low pass filter)
filters noise out of the
amplified signal. BIO filter 33 can comprise an approximately 3kHz filter. In
some
embodiments, BIO filter 33 comprises an approximately 7.5kHz filter, such as
when system
100 is configured to accommodate pacing of the heart (e.g. to avoid
significant signal loss
and/or degradation during pacing of the heart).
[0124] BIO filter 33 can include differential amplifier stages used to
remove common
mode power line signals from the bio-potential data. This differential
amplifier can
implement a baseline restore function which removes DC offsets and/or low
frequency
artifacts from the bio-potential signals. In some embodiments, this baseline
restore function
comprises a programmable filter which can comprise one or more filter stages.
In some
embodiments, the filter includes a state dependent filter. Characteristics of
the state
dependent filter can be based on a threshold and/or other level of a parameter
(e.g. voltage),
with the filter rate varied based on the filter state. Components of the
baseline restore
function can incorporate noise reduction techniques such as dithering and/or
pulse width
modulation of the baseline restore voltage. The baseline restore function can
also determine,
by measurement, feedback, and/or characterization, the filter response of one
or more stages.
The baseline restore function can also determine and/or discriminate the
portions of the signal
representing a physiological signal morphology from an artifact of the filter
response and
computationally restore the original morphology, or a portion thereof. In some
embodiments,
the restoration of the original morphology can include subtraction of the
filter response
directly and/or after additional signal processing of the filter response,
such as via static,
temporally-dependent, and/or spatially-dependent weighting, multiplication,
filtering,
inversion, and combinations of these. In some embodiments, the baseline
restore function is
implemented in BIO filter 33, BIO processor 36, or both.
[0125] The LOC signal path 40 includes a high voltage buffer 41 coupled to
the DFIB
protection module 22. In this embodiment, the high voltage buffer 41 is
configured to
accommodate the relatively high voltages used in treatment techniques, such as
RF ablation
voltages. For example, the high voltage buffer can have 100V power-supply
rails. In some
embodiments, each high voltage buffer 41 has a high input impedance, such as
an impedance
of 100 kiloohms to 10 megaohms at the localization frequencies. In some
embodiments, all
high voltage buffers 41, taken together as a total parallel electrical
equivalent, also has a high
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input impedance, such as an impedance of 100 kiloohms to 10 megaohms at the
localization
frequencies. In some embodiments, the high voltage buffer 41 has a bandwidth
that maintains
good performance over a range of high frequencies, such as frequencies between
100
kilohertz and 10 megahertz, such as frequencies of approximately 2 megahertz.
In some
embodiments, the high voltage buffer 41 does not include a passive RF filter
input stage, such
as when the high voltage buffer 41 has a +100V power-supply. A high frequency
bandpass
filter 42 can be coupled to the high voltage buffer 41, and can have a
passband frequency
range of about 20kHz to 80kHz for use in localization. In some embodiments,
the filter 42
has low noise with unity gain (e.g. a gain of 1 or about 1).
[0126] The US signal path 60 comprises an US isolation multiplexer, MUX 61,
a US
transformer with a Tx/Rx switch, US transformer 62, a US generation and
detection module
63, and an US signal processor 66. The US isolation MUX 61 is connected to the
DFIB
protection module 22, and is used for turning on/off the US transducers 12b,
such as in a
predetermined order or pattern. The US isolation MUX 61 can be a set of high
input
impedance switches that, when open, isolate the US system and remaining US
signal path
elements, decoupling the impedance to ground (through the transducers and the
US signal
path 60) from the input of the LOC and BIO paths. The US isolation MUX 61 also
multiplexes one transmit/receive circuit to one or more multiple transducers
12b on the
catheter 10. The US transformer 62 operates in both directions between the US
isolation
MUX 61 and the US generation and detection module 63. US transformer 62
isolates the
patient from the current generated by the US transmit and receive circuitry in
module 63
during ultrasound transmission and receiving by the US transducers 12b. The US
transformer
62 can be configured to selectively engage the transmit and/or receive
electronics of module
63 based on the mode of operation of the transducers 12b, for example by using
a
transmit/receive switch. That is, in a transmit mode, the module 63 receives a
control signal
from a US processor 66 (within a data processor 26) that activates the US
signal generation
and connects an output of the Tx amplifier to US transformer 62. The US
transformer 62
couples the signal to the US isolation MUX 61 which selectively activates the
US transducers
12b. In a receive mode, the US isolation MUX 61 receives reflection signals
from one or
more of the transducers 12b, which are passed to the US transformer 62. The US
transformer
62 couples signals into the receive electronics of the US generation and
detection module 63,
which in-turn transfers reflection data signals to the US processor 66 for
processing and use
by the user interface 27 and display 27a. In some embodiments, processor 66
commands
MUX 61and US transformer 62 to enable transmission and reception of ultrasound
to activate
1
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one or more of the associated transducers 12b, such as in a predetermined
order or pattern.
The US processor 66 can include, as examples, detection of a single, first
reflection, the
detection and identification of multiple reflections from multiple targets,
the determination of
velocity information from Doppler methods and/or from subsequent pulses, the
determination
of tissue density information from the amplitude, frequency, and/or phase
characteristics of
the reflected signal, and combinations of one or more of these.
[0127] An analog-to-digital converter (ADC) 24 is coupled to the BIO filter
33 of the
BIO signal path 30 and to the high frequency filter 42 of the LOC signal path
40. Received
by the ADC 24 is a set of individual time-varying analog bio-potential voltage
signals, one
for each electrode 12a. These bio-potential signals have been differentially
referenced to a
unipolar electrode for enhanced common mode rejection, filtered, and gain-
calibrated on an
individual channel-by-channel basis, via BIO signal path 30. Received by the
ADC is also a
set of individual time-varying analog localization voltage signals for each
axis of each patch
electrode 56, via LOC signal path 40, which are output to the ADC 24 as a
collection of 48
(in this embodiment) localization voltages measured at a single time for the
electrodes 12a.
The ADC 24 has high oversampling to allow noise shaping and filtering, e.g.
with an
oversampling rate of about 625kHz. In some embodiments, sampling is performed
at or
above the Nyquist frequency of system 100. The ADC 24 is a multi-channel
circuit that can
combine BIO and LOC signals or keep them separate. In one embodiment, as a
multi-
channel circuit, the ADC 24 can be configured to accommodate 48 localization
electrodes
12a and 32 auxiliary electrodes (e.g. for ablation or other processes), for a
total of 80
channels. In other embodiments, more or less channels can be provided. In FIG.
1, for
example, almost all of the elements of console 20 can be duplicated for each
channel (e.g.
except for the UI system 27). For example, console 20 can include a separate
ADC for each
channel, or an 80 channel ADC. In this embodiment, signal information from the
BIO signal
path 30 and the LOC signal path 40 are input to and output from the various
channels of the
ADC 24. Outputs from the channels of the ADC 24 are coupled to either the BIO
signal
processing module 34 or the LOC signal processing module 44, which pre-process
their
respective signals for subsequent processing as described herebelow. In each
case, the
preprocessing prepares the received signals for the processing by their
respective dedicated
processors discussed herebelow. The BIO signal processing module 34 and the
LOC signal
processing module 44 can be implemented in firmware, in whole or in part, in
some
embodiments.
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[0128] The bio-potential signal processing module 34 can provide gain and
offset
adjustment and/or digital RF filtering having a non-dispersive low pass filter
and an
intermediate frequency band. The intermediate frequency band can eliminate
ablation and
localization signals. The bio-potential signal processing module 34 can also
include digital
bio-potential filtering, which can optimize the output sample rate.
[0129] Additionally, the bio-potential signal processing module 34 can also
include "pace
blanking", which is the blanking of received information during a timeframe
when, for
example, a physician is "pacing" the heart. Temporary cardiac pacing can be
implemented
via the insertion or application of intracardiac, intraesophageal, and/or
transcutaneous pacing
leads, as examples. The goal in temporary cardiac pacing can be to
interactively test and/or
improve cardiac rhythm and/or hemodynamics. To accomplish the foregoing,
active and
passive pacing trigger and input algorithmic trigger determinations can be
performed (such as
by system 100). The algorithmic trigger determination can use subsets of
channels, edge
detection and/or pulse width detection to determine if pacing of the patient
has occurred.
Optionally, pace blanking can be applied by system 100 on all channels or
subsets of
channels, including channels on which detection did not occur.
[0130] Additionally, the bio-potential signal processing module 34 can also
include
specialized filters that remove ultrasound signals and/or other unwanted
signals (e.g. artifacts
from the bio-potential data). In some embodiments, to perfoim this filtering,
edge detection,
threshold detection and/or timing correlations are used.
[0131] The localization signal processing module 44 can provide individual
channel/frequency gain calibration, IQ demodulation with tuned demodulation
phase,
synchronous and continuous demodulation (without MUXing), narrow band R
filtering,
and/or time filtering (e.g. interleaving, blanking, etc.), as discussed
herebelow. The
localization signal processing module 44 can also include digital localization
filtering, which
optimizes the output sample rate and/or frequency response.
[0132] In this embodiment, the algorithmic computations for the BIO signal
path 30,
LOC signal path 40, and US signal path 60 are performed in console 20. These
algorithmic
computations can include but are not limited to: processing multiple channels
at one time,
measuring propagation delays between channels, turning x, y, z data into a
spatial distribution
of electrode locations, including computing and applying corrections to the
collection of
positions, combining individual ultrasound distances with electrode locations
to calculate
detected endocardial surface points, and constructing a surface mesh from the
surface points.
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The number of channels processed by console 20 can be between 1 and 500, such
as between
24 and 256, such as 48, 80, or 96 channels.
[0133] A data processor 26, which can include one or more of a plurality of
types of
processing circuits (e.g. a microprocessor) and memory circuitry, executes
computer
instructions necessary to perform the processing of the pre-processed signals
from the BIO
'signal processing module 34, localization signal processing module 44, and US
TX/RX MUX
61. The data processor 26 can be configured to perform calculations, as well
as perform data
storage and retrieval, necessary to perform the functions of system 100.
[0134] In this embodiment, data processor 26 can include a bio-potential
(BIO) processor
36, a localization (LOC) processor 46, and an ultrasound (US) processor 66.
The bio-
potential processor 36 can perform processing of recorded, measured, or sensed
bio-
potentials (e.g., from electrodes 12a). The LOC processor 46 can perfolin
processing of
localization signals. The US processor 66 can perform image processing of the
reflected US
signals, (e.g. from transducers 12b).
[0135] Bio-potential processor 36 can be configured to perform various
calculations. For
example, BIO processor 36 can include an enhanced common mode rejection
filter, which
can be bidirectional to minimize distortion and which can be seeded with a
common mode
signal. BIO processor 36 can also include an optimized ultrasound rejection
filter and be
configured for selectable bandwidth filtering. Processing steps for data in US
signal path 60
can be performed by bio signal processor 34 and/or bio processor 36.
[0136] Localization processor 46 can be configured to perform various
calculations. As
discussed in more detail herebelow, LOC processor 46 can electronically make
(calculate)
corrections to an axis based on the known shape of electrode array 12, make
corrections to
the scaling or skew of one or more axes based on the known shape of the
electrode array 12,
and perform "fitting" to align measured electrode positions with known
possible
configurations, which can be optimized with one or more constraints (e.g.
physical
constraints, such as distance between two electrodes 12a on a single spline,
distance between
two electrodes 12a on two different splines, maximum distance between two
electrodes 12a,
minimum distance between two electrodes 12a, and/or minimum and/or maximum
curvature
of a spline, and the like).
[0137] US processor 66 can be configured to perform various calculations
associated
with generation of the US signal via the US transducers 12b and processing US
signal
reflections received by the US transducers 12b. US processor 66 can be
configured to
interact with the US signal path 60 to selectively transmit and receive US
signals to and from
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the US transducers 12b. The US transducers 12b can each be put in a transmit
mode and/or a
receive mode under control of the US processor 66. The US processor 66 can be
configured
to construct a 2D and/or 3D image of the heart chamber (HC) within which the
electrode
array 12 is disposed, using reflected US signals received from the US
transducers 12b via the
US path 60.
[0138] Console 20 can also include localization driving circuitry,
including a localization
signal generator 28 and a localization drive current monitor circuit 29. The
localization drive
circuitry provides high frequency localization drive signals (e.g. 10kHz -
1MHz, such as
10kHz - 100kHz). Localization using drive signals at these high frequencies
reduces the
cellular response effect on the localization data (e.g. from blood cell
deformation), and/or
allows higher drive currents (e.g. to achieve a better signal-to-noise ratio).
Signal generator
28 produces a high resolution digital synthesis of a drive signal, (e.g. a
sine wave), with ultra-
low phase noise timing. The drive current monitoring circuitry provides a high
voltage, wide
bandwidth current source, which is monitored to measure impedance of the
patient P.
[0139] Console 20 can also include at least one data storage device 25, for
storing various
types of recorded, measured, sensed, and/or calculated information and data,
as well as
program code embodying functionality available from the console 20.
[0140] Console 20 can also include a user interface (UI) system 27
configured to output
1
results of the localization, bio-potential, and US processing. UI system 27
can include at
least one display 27a to graphically render such results in 2D, 3D, or a
combination thereof.
In some embodiments, the display 27a includes two simultaneous views of the 3D
results
with independently configurable view/camera properties, such as view
directions, zoom level,
pan position, and object properties, such as color, transparency, brightness,
luminance, etc.
UI System 27 can include one or more user input components, such as a touch
screen, a
keyboard, a joystick, and/or a mouse.
[0141] Console 20, or another component of system 100, can include one or
more
algorithms, such as complexity algorithm 600 shown. Complexity algorithm 600
can
comprise an algorithm as described herebelow in reference to Fig. 3.
Complexity algorithm
600 can include one or more algorithms, such as one or more of: CV algorithm
200, LRA
algorithm 300, LIA algorithm 400, FA algorithm 500, and/or complexity
algorithm 600
described herebelow. Complexity algorithm 600 can identify, quantify,
categorize, and/or
otherwise assess cardiac conduction patterns or characteristics, such as to
produce diagnostic
information, diagnostic results 1100 herein. Complexity algorithm 600 can
produce an
assessment, over time and/or space, of complexity and/or an assessment of a
variation of
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complexity over time. In some embodiments, complexity algorithm 600, and/or
another
algorithm of system 100, comprises a bias. In some embodiments, the algorithm
comprises a
bias toward false positives (e.g. a bias towards falsely identifying a non-
complex region as
being complex, versus not classifying a complex region as being complex). In
some
embodiments, the algorithm comprises a bias toward false negatives. In some
embodiments,
an algorithm of system 100 comprises a bias that is set and/or adjusted ("set"
herein) by a
clinician, such as to bias system 100 toward a particular preference of the
clinician.
[0142] Complexity, as determined by the algorithms of the present inventive
concepts,
includes any deviation from the expected or normal behavior of what would
otherwise be a
simple, repetitive, and consistent pattern of electrical activity. In cardiac
electrical activity,
the expected or normal behavior of the heart chamber is consistent,
repetitive, and
coordinated activation of the tissue, called sinus rhythm, that initiates at a
location (e.g. the
sino-atrial node) and propagates along the chamber smoothly. Complexity
includes any
deviation that disrupts the consistency (e.g. time, amplitude, direction,
and/or repetition rate
of activation), and/or coordination/order (e.g. time and/or direction of
activation). Regions of
tissue may self-initiate electrical activation (automaticity), interrupting
otherwise coordinated
activation. Regions of tissue that may be compromised, scarred, diseased
and/or possess
otherwise heterogenous characteristics (e.g. fibrosis, varying fiber
orientations, varying endo-
cardial to epicardial pathways, and the like) can create complexity of cardiac
activity, as
described hereabove. A region that creates complexity may disrupt the expected
conduction
in a consistent way. For example, conduction may be redirected in a different
direction and
with a reduction in amplitude, but can do so in the same way for each
activation. F.
Alternatively, a region that exhibits complexity (e.g. as identified by an
algorithm of system
100), may disrupt the expected conduction in a stochastic or probabilistic way
(e.g. seemingly
random variation), but in a way that possesses a recognizable statistical
behavior in how it
disrupts conduction. For example, modified conduction can be identified
through a region in
one characteristic manner for X% of the time, and in a second, different
characteristic
manner, for Y% of the time. In some embodiments, for Z% (where Z < 100) of the
time, the
activation exhibits normal conduction, however the region is still identified
by system 100 as
complex due to modified conduction, in one or more forms, for some portion of
the time.
[0143] The algorithms of the present inventive concepts can be configured
to identify
when multiple regions of complexity interact, or otherwise couple, in ways
that create further
complexity across the cardiac chamber, thereby compounding the degree of
global
complexity over the heart chamber, such as is described herebelow in reference
to Fig. 3A.
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Because the cardiac tissue has propagative properties with a refractory (non-
active) period,
complexity that impacts the order and timing of activation can have
lasting/persisting effects
on later activations in time, and across a broad spatial area. Therefore, as
the number of
unique or discrete zones of automaticity or heterogeneity increases (tissue-
mediated
complexity), the resulting electrical activation becomes increasingly complex
(e.g. a
compounding of both tissue-mediated complexity and coupling-related
complexity), tied
together in time and space by the propagating nature of cardiac tissue,
established by the
variations in conduction preceding, and affecting variations in conduction to
follow. As the
complexity increases, the ability to identify the tissue-mediated complexity
from the
coupling-related complexity based on simple electrical measurements becomes
more
difficult. System 100 can be configured to gather more information over time
and across
space (e.g. simultaneously), with the additional information gathered to aid
in one or more
algorithms decoding the complexity locally, regionally, and globally across
the chamber.
[0144] Complexity algorithm 600 can perform a complexity assessment based
on
calculated electrical activity data 120b that represents multiple vertices,
such as when the
associated recorded electrical activity data 120a comprises data recorded from
at least three
recording locations within a heart chamber (e.g. on and/or offset from the
heart wall). In
some embodiments, the recorded electrical activity data 120a includes at least
one location
offset from the walls of the heart (e.g. at least one non-contact recording).
In some
embodiments, the recorded electrical activity data 120a includes at least one
location on a
wall of the heart (e.g. at least one contact recording). In some embodiments,
the recorded
electrical activity data 120a includes at least one location offset from the
walls of the heart,
and at least one location on a wall of the heart (e.g. at least one contact
and one non-contact
recording, a 'hybrid'). In some embodiments, for each location on the heart
wall in which a
contact-based measurement is made, system 100 is biased to categorize that
location as a
vertex.
[0145] In some embodiments, algorithm 600 comprises a second algorithm
configured to
calculate surface charge data and/or dipole density data for each of the
multiple vertices,
based on the recorded electrical activity data 120a (e.g. recorded voltages),
such as when the
complexity analysis is based on surface charge data and/or dipole density
data. Surface
charge data and/or dipole density data can be calculated as described in
applicant's United
States Patent Number 8,417.313, titled "METHOD AND DEVICE FOR DETERMINING
AND PRESENTING SURFACE CHARGE AND DIPOLE DENSITIES ON CARDIAC
WALLS", issued April 9, 2013, and United States Patent Number 8,512,255,
titled "DEVICE
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AND METHOD FOR THE GEOMETRIC DETERMINATION OF ELECTRICAL DIPOLE
DENSITIES ON THE CARDIAC WALL", issued August 20, 2013, the content of each of
which is incorporated herein by reference in its entirety for all purposes. In
some
embodiments, algorithm 600 comprises a third algorithm that converts the
surface charge
data and/or the dipole density data into surface voltage data, such as when
the complexity
analysis is based on the surface voltage data.
[0146] In some embodiments, algorithm 600 performs a complexity assessment
over a
relatively small portion of the patient's heart (e.g. a relatively small
portion of a patient's
heart chamber), such as a portion that represents no more than 7cm2 of the
heart wall, such as
no more than 4cm2, such as no more than 1cm2. In these embodiments, electrical
activity can
be recorded (e.g. by electrodes 12a) from at least three recording locations,
and calculated
electrical activity data 120b can be determined for at least 3 vertices (as
described herein). In
some embodiments, the at least three recording locations comprise at least
three locations on
the heart wall (e.g. via a contact-based recording). In some embodiments, at
least one
recording location is offset from the heart wall (e.g. non-contact mapping).
In some
embodiments, algorithm 600 performs the small portion complexity assessment
using voltage
data and/or dipole density data. In some embodiments, analysis of a small
portion of the
patient's heart is performed with system 100 and the associated method
described herebelow
in reference to Figs. 9 and 9A.
[0147] In some embodiments, algorithm 600 performs a complexity assessment
over a
moderate or large portion of the patient's heart, such as a portion of the
patient's heart
representing at least 7cm2 of heart wall tissue (e.g. wall tissue of an atria
of the heart), such as
a minimum surface area of 1cm2, such as 4cm2, such as 7cm2. In these
embodiments,
electrical activity can be recorded (e.g. by electrodes 12a) from at least 24
locations within
the heart (e.g. within a single heart chamber), and calculated electrical
activity data 120b can
be determined for at least 64 vertices. In some embodiments, electrical
activity can be
recorded from at least 24 heart wall locations (e.g. via a contact-based
recording), with or
without additional recordings made offset from the heart wall (e.g. in the
flowing blood via a
non-contact-based recording). In these embodiments, electrical activity can be
recorded from
at least 48 heart wall locations, or at least 64 heart locations. In some
embodiments,
electrical activity is recorded from both locations on the heart wall and
offset from the heart
wall, such as when data is recorded from at least 24, at least 48, or at least
54 contact and
non-contact locations within the heart chamber. In these embodiments,
calculated electrical
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activity data 120b can be determined for at least 100 vertices, such as at
least 500, at least
3000, and/or at least 5000 vertices.
[0148] In some embodiments, the complexity algorithm 600 incorporates data
through
various depths (e.g. layers) of tissue. In thicker tissues, electrical
conduction can vary
through the thickness. The stretch and/or strain of the tissue can also have
an impact on the
conduction properties of the tissue. Measuring, recording, and/or calculating
electrical data
or biomechanical data through the depth of tissue can be used to improve the
accuracy and/or
specificity of complexity algorithm 600. In some embodiments, surface charge
density
and/or dipole density is calculated through a thickness of tissue of the
cardiac chamber, with
the calculated data used as input to complexity algorithm 600. In some
embodiments, surface
charge density and/or dipole density are determined as described in
applicant's co-pending
United States Patent Application Serial Number 15/926,187, titled "DEVICE AND
METHOD FOR THE GEOMETRIC DETERMINATION OF ELECTRICAL DIPOLE
DENSITIES ON THE CARDIAC WALL", filed March 20, 2018, the content of which is
incorporated herein by reference in its entirety for all purposes.
[0149] Complexity algorithm 600 can assess the variation of one or more
characteristics,
such as electrical, mechanical, functional, and/or physiologic characteristics
of the heart that
vary in time, space, magnitude and/or state. Studies of cardiac behavior,
function, and other
characteristics, over the last several decades have yielded a substantive
understanding of what
is considered "normal". Cardiac conditions such as cardiac arrhythmias exhibit
variations
from the norm in many ways, and these variations can be quantified, qualified,
and/or
otherwise assessed by complexity algorithm 600.
[0150] In some embodiments, variations in time or temporal repetition
and/or stability
(e.g. measures of temporal regularity and/or irregularity) indicate the
presence of a cardiac
arrhythmia. Electrical characteristics (e.g. cycle length, dominant frequency,
harmonic
organization, fractionation or measures of waveform "energy", Shannon entropy,
waveform
deflections within a time window, temporal wave recurrence, regularity, and/or
higher order
statistics of the electrical data, such as kurtosis) can be measured or
otherwise determined by
system 100, and these characteristics can be included in the assessment
performed by
complexity algorithm 600. System 100 can determine these variables using tools
such as:
interval analysis; Fourier, Hilbert or other transforms; wavelet analysis; and
combinations of
these.
[0151] Mechanical and/or functional ("mechanical" herein) characteristics
assessed by
algorithm 600 can include deflection timing of the heart wall over time. In
some
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embodiments, system 100 determines, and algorithm 600 assesses a combination
of electrical,
and/or mechanical data, such as electro-mechanical delay (e.g. which can also
vary as a
function of time).
[0152] In some embodiments, algorithm 600 assesses a variation in magnitude
and/or
state of a characteristic determined by system 100. For example, electrical
characteristics
assessed can include an assessment of electrical activity at a cardiac
surface, such as an
assessment of: rms amplitude; peak-to-peak amplitude; peak-negative amplitude;
and
combinations of these. Mechanical characteristics assessed can include total
or average
deflection of the heart wall through one or more phases of the cardiac cycle.
In some
embodiments, a combination of electrical and mechanical data includes ratios
of electrical
magnitude to mechanical magnitude and/or functional efficiency.
[0153] In some embodiments, algorithm 600 assesses a variation over space
or in
direction of one or more characteristics. For example, electrical
characteristics assessed can
include: directional bipoles formed in different directions (e.g. determined
from data recorded
by unipolar electrodes); conduction velocity direction; spatial wave analysis;
and
combinations of these. In some embodiments, a Laplacian operator can be
applied to
electrical activity data 120a recorded from a multi-polar and/or omni-polar
catheter to
provide calculated data for algorithm 600 to assess.
[0154] In some embodiments, algorithm 600 assesses variations in one or
more
characteristics, in two or more of: time; space; magnitude; and/or state. In
some
embodiments, algorithm 600 assesses two or more of these that vary
simultaneously, such as
a temporospatial variation. In these embodiments, algorithm 600 can assess
electrical
characteristics to determine if a pattern of interest occurs (e.g. focal,
rotational, irregular,
directional, and/or timing patterns). Algorithm 600 can assess temporospatial
features or
patterns, such as an activation sequence or conduction pattern that exhibits
one or more of the
following characteristics: propagation that 'breaks out' through a confined
'gap' or opening,
regionally constrained pivoting re-entry, and other irregular conduction
patterns (e.g. patterns
that vary in time and space), rotation about a central core or obstacle,
and/or focal activation
spreading from a single location. Algorithm 600 can include an assessment of
changes in
conduction velocity (e.g. magnitude and/or direction). Algorithm 600 can
perform any
qualitative and/or quantitative analysis of one or more of these
characteristics, such as to
provide an assessment of complexity.
[0155] The complexity assessment provided by algorithm 600 can comprise a
binary
measure of whether the complexity occurred at one or more times at each
location (e.g. each
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vertex) assessed. The complexity assessment provided by algorithm 600 can
comprise a
static level of complexity across a time period (e.g. a sum, average, median,
variance,
standard deviation, and/or percentile level). Static levels determined can be
thresholded to
calculate and/or display a subset range of the static data. The complexity
assessment
provided by algorithm 600 can comprise an assessment of change in complexity
over time
(e.g. over one or more time periods), such as an assessment of changes in
rate, frequency,
degree, percentile and/or probability. Complexity algorithm 600 can perform
multiple
complexity assessments in sequence, such as using a "rolling window" as
described
herebelow in reference to Fig. 8. The multiple complexity assessments can
include an
assessment of a static quantity of complexity over time.
[0156] Complexity algorithm 600 can assess complexity (e.g. changes in
complexity) and
produce results (e.g. diagnostic results 1100) that are used for multiple
purposes. For
1
example, algorithm 600 can provide an assessment of the stability and/or
consistency of
complexity, and/or other arrhythmogenic conditions, based on an analyzed
recording duration
of a few minutes or less (e.g. a duration of less than 10 minutes). The
assessment can
differentiate areas of consistent complexity versus transient or intermittent
complexity.
Regions of consistency can be correlated to specific tissue substrate
characteristics. In the
cardiac system, areas where the tissue substrate is anisotropic,
heterogeneous, abnormal or
diseased may consistently create variation and/or complexity in the electrical
activity at that
tissue location. However, areas of normal tissue may also see variation or
other complexity
(wave collisions, interference, fusion, functional block, and the like)
resulting from
downstream interaction of complex propagating wavefronts created by
anisotropic areas of
the tissue substrate. This complexity is a "functional" effect where the
electrophysiological
interactions of propagating waves can cause these waves to interfere or
interact with one
another in complex ways, often intermittently. Because cardiac tissue remains
in a refractory
(unable to be re-activated) state for a period of time following each
activation, the functional
effect occurs not only at the moment when a wave of activation passes, but for
an extended
period after it has passed. The net result is that complexity of cardiac
tissue activation, as
identified by complexity algorithm 600, can also occur in areas where the
tissue itself is not
abnormal or diseased, but is rather due to the prior complex interactions that
occurred at other
tissue locations. Fixed, substrate-mediated complexity (or mechanisms) will
probabilistically
re-occur at the same location. Functional complexity may vary in location and
frequency of
occurrence at a given location. Complexity algorithm 600 can be configured to
assess the
consistency, stability, repeatability, and/or pattern of complexity to
differentiate between
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fixed, substrate-mediated complexity vs. functional complexity, as described
herebelow in
reference to Fig. 3A.
[0157] Complexity algorithm 600 can be used to determine electrical changes
resulting
from a delivered therapy (e.g. an RE' or other cardiac ablation, such as a
therapy provided by
treatment subsystem 800, as described herebelow). Comparison of complexity
and/or
consistency of complexity ("complexity" herein) before and after a therapeutic
activity or
interval can be used to indicate the electrophysiological impact of the
delivered therapy.
Algorithm 600 can provide a comparison in the form of a difference plot.
Therapeutic events
may be as short as a few seconds (at a single or small number of locations) or
up to many
minutes (for more extensive maneuvers such as ablative lines, loops, cores,
boxes, and the
like). The longer the therapeutic activity or interval, the more change may
exist in the
comparison. In some embodiments, system 100 provides a real time (e.g. during
therapy)
feedback-loop of cause (therapy) and effect (complexity assessment, such as a
change in
complexity prior to and after therapy). System 100 can be configured to
provide a
complexity assessment (e.g. recorded electrical activity data 120a and
calculate complexity
via algorithm 600) in a relatively short period of time (e.g. less than 10
minutes, or less than 5
minutes), such that the clinician is more likely to reduce therapeutic
interval times to assess
complexity after each interval. In these embodiments, unnecessary ablations
can be avoided
and/or overall procedure time can be reduced.
[0158] Complexity algorithm 600 can be configured to produce complexity
data (e.g. the
output of a complexity assessment) in real time, such that the complexity data
(e.g. diagnostic
results 1100) can be shown dynamically, also in real time. For example, system
100 can
record and process electrical activity data 120a, and algorithm 600 can
analyze the recorded
activity, such as using a rolling window (e.g. as described herebelow in
reference to Fig. 8),
such as a time window with a duration of between 5 seconds and 60 seconds.
Algorithm 600
provides multiple complexity assessments by continuously analyzing recorded
electrical
activity data 120a over the total duration assessed, with newer data added and
oldest data
excluded as the electrical activity data 120a recording continues. Complexity
assessments
(e.g. multiple complexity assessments provided in a video format) can be
provided in real
time (e.g. with a short processing delay), such as during a treatment (e.g.
ablation) to
dynamically determine when the treatment has achieved a desired result (e.g.
sufficient
energy has been delivered to cause the desired effect, such as electrical
block), and/or how to
modify the therapy to achieve a therapeutic goal or otherwise improve
efficiency.
Alternatively or additionally, the provided complexity assessments can be
visualized (e.g. in
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a playback mode) one or more times after the associated recording of
electrical activity data
120a has ceased, such as to perform additional therapy and/or modify the
therapy.
[0159] Complexity algorithm 600 can provide complexity assessments based on
electrical
activity data 120 (and/or additional patient data 150 as described herebelow)
recorded during
two separate clinical procedures (e.g. a first clinical procedure and a
subsequent, second
clinical procedure). Algorithm 600 can provide one or more complexity
assessments for each
clinical procedure, such as to allow a comparison to be made between
assessments from two
different procedures (e.g. an assessment made by algorithm 600). The second
clinical
procedure can be separated from the first clinical procedure by days, weeks,
months, or years.
A comparative assessment made by algorithm 600 can assess the therapeutic
effects of the
first procedure and the recovery (e.g. healing) of the cardiac tissue or the
adaptation of the
cardiac tissue in the interim between procedures. Cardiac tissue may adapt in
response to the
altered electrical characteristics (e.g. altered patterns, rhythms, and the
like, such as from
electrical remodeling), and/or the altered mechanical characteristics (e.g.
function) of the
tissue, each as caused by the preceding therapeutic procedure. Techniques used
in the second
clinical procedure can be based on these above assessments provided by
algorithm 600 (e.g.
in the form of diagnostic results 1100), such as the tissue response (e.g. the
electrical and
mechanical response described hereabove) to the therapy provided in the first
procedure.
[0160] While algorithm 600 has been described hereabove as analyzing
electrical activity
data 120, in some embodiments, algorithm 600 further includes in its
assessment, an analysis
of "additional patient data" recorded by system 100 (e.g. the complexity
assessment is
based on additional patient data 150 recorded by system 100 as well as
electrical activity data
120 and anatomical data 110 described hereabove). For example, system 100 can
comprise
one or more functional elements configured as sensors, such as functional
element 99 of
catheter 10, functional element 899 of treatment catheter 800 described
herebelow, and/or
functional element 199 of system 100. Functional element 99 of catheter 10 can
comprise
one or more sensors positioned on an expandable spline of electrode array 12
(as shown),
and/or on shaft 16. Functional element 199 of system 100 can comprise a sensor
positioned
proximate the patient (e.g. on the skin of the patient or relatively near the
patient) and/or a
sensor positioned within the patient (e.g. temporarily or chronically
positioned under the
patient's skin). In some embodiments, one or more electrodes 12a and/or
ultrasound
transducers 12b are configured to record the additional patient data 150.
[0161] In some embodiments, sensor-based functional elements 99, 199,
and/or 899
comprises a sensor selected from the group consisting of: an electrode or
other sensor for
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recording electrical activity; a force sensor; a pressure sensor; a magnetic
sensor; a motion
sensor; a velocity sensor; an accelerometer; a strain gauge; a physiologic
sensor; a glucose
sensor; a pH sensor; a blood sensor; a blood gas sensor; a blood pressure
sensor; a flow
sensor; an optical sensor; a spectrometer; an interferometer; a measuring
sensor, such as to
measure size, distance, and/or thickness; a tissue assessment sensor; and
combinations of one,
two, or more of these.
[0162] Additional patient data recorded by system 100 (e.g. via catheter
10, functional
element 199, functional element 899, and/or other sensor of system 100), can
include patient
mechanical information; patient physiologic information; and/or patient
functional
information. Additional data recorded by system 100 can include data related
to a patient
parameter selected from the group consisting of: heart wall motion; heart wall
velocity; heart
tissue strain; magnitude and/or direction of heart blood flow; vorticity of
blood; heart valve
mechanics; blood pressure; tissue properties, such as density, tissue
characteristics and/or
biomarkers for tissue characteristics, such as metabolic activity or
pharmaceutical uptake;
tissue composition (e.g. collagen, myocardium, fat, connective tissue); and
combinations of
one, two, or more of these.
[0163] As described hereabove, one or more complexity assessments performed
by
algorithm 600 can be based on this additional patient data, such as when both
electrical
activity data 120 and additional patient data 150 is included in the analysis
performed. In
some embodiments, the complexity assessment performed by algorithm 600
comprises an
assessment of one or more of: electrical-mechanical delay of tissue; magnitude
ratio of an
electrical to a mechanical characteristic; and combinations of these.
[0164] Additional patient data 150 can also comprise prior data (e.g. data
collected during
a prior procedure) from the same patient or prior data from a set of
historical patients other
than the patient being diagnosed or treated. The data can be used to form a
computational
model into which the existing patient's data is fitted, classified, ranked,
prioritized,
optimized, and/or otherwise assessed as described above.
[0165] Diagnostic results 1100 can comprise measured data and/or data
resulting from an
analysis of measured data (e.g. an analysis of recorded electrical activity
data 120a and/or
anatomical data 110). Diagnostic results 1100 can be provided (e.g. provided
to a clinician of
the patient), in one or more forms, such as when displayed on display 27a,
provided audibly
(e.g. by a speaker of system 100), and/or provided in a printed report (e.g.
by a printer of
system 100). Diagnostic results 1100 can be used by a clinician to customize a
therapy for
the patient, such as to determine at which locations to ablate tissue in a
cardiac ablation
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procedure, such as is described in applicant's co-pending United States Patent
Application
k Serial Number 14/422,941, titled "CATHETER, SYSTEM AND METHODS OF
MEDICAL
USES OF SAME, INCLUDING DIAGNOSTIC AND TREATMENT USES FOR THE
HEART", filed February 20, 2015, the content of which is incorporated herein
by reference
in its entirety for all purposes.
[0166] In some embodiments, diagnostic results 1100 are based on a
complexity
assessment performed by complexity algorithm 600 for a single heart wall
location or
multiple heart wall locations. The single and/or multiple location diagnostic
results 1100 can
be presented to a user (e.g. the patient's clinician) in reference to an image
of the patient's
anatomy (e.g. via display 27a). Diagnostic results 1100 can comprise an
assessment of
complexity over time, such as an assessment of complexity over a pre-
determined time
duration.
[0167] As described hereabove, system 100 can be configured to
perform a medical
procedure (e.g. a diagnostic, prognostic, and/or therapeutic procedure)
related to an
arrhythmia or other cardiac condition of the patient. System 100 can be
configured to
perform a medical procedure on a patient with a cardiac condition selected
from the group
consisting of: atrial fibrillation; atrial flutter; atrial tachycardia; atrial
bradycardia, ventricular
tachycardia; ventricular bradycardia; ectopy; congestive heart failure;
angina; arterial
stenosis; and combinations of one, two, or more of these. In some embodiments,
system 100
performs a medical procedure on a patient that exhibits heterogeneous
activation, conduction,
depolarization, and/or repolarization that varies in time, space, magnitude,
and/or state (e.g.
combinations, such as velocity). Electrical activity of the patient's heart
may contain patterns
that can be detected or mapped by system 100, such as patterns selected from
the group
consisting of: focal; re-entrant; rotational; pivoting; irregular (e.g. in
direction and/or
velocity); functional block; permanent block; and combinations thereof.
[0168] System 100 can include devices or agents (e.g. pharmaceutical
agents), treatment
subsystem 800, for treating a patient (e.g. treating one or more cardiac
conditions of the
patient). In the embodiment shown in Fig. 1, treatment subsystem 800 includes
a treatment
catheter 850, including shaft 860, which can be configured to be advanced
through the
patient's vasculature into one or more chambers of the patient heart, using
standard
interventional techniques. In some embodiments, the distal portion of shaft
860 is advanced
into the patient's left atrium via a transseptal sheath, not shown but such as
a standard device
used in left atrial ablation procedures. Treatment catheter 850 comprises
treatment element
870 on the distal end (as shown) or at least the distal portion of shaft 860.
Treatment element
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870 can comprise one or more treatment elements, such as one or more energy
delivery
elements configured to deliver energy to ablate cardiac tissue (e.g. ablation
energy delivered
to the heart wall). Treatment element 870 can include an array (e.g. a linear
or other array) of
treatment elements. Treatment element 870 can comprise one or more electrodes
configured
to deliver radiofrequency (RF) or other electromagnetic energy to tissue. In
some
embodiments, treatment element 870 comprises one or more energy delivery
elements
configured to deliver energy in a foiln selected from the group consisting of:
electromagnetic
energy such as RF energy and/or microwave energy; thermal energy such as heat
energy
and/or cryogenic energy; light energy such as laser light energy; sound energy
such as
ultrasound energy; chemical energy; mechanical energy; and combinations of
these. In some
embodiments, treatment element 870 comprises one or more agent delivery
elements (e.g.
one or more needles, iontophoretic elements, and/or fluid jets) configured to
deliver an agent
(e.g. a pharmaceutical agent) into cardiac tissue or other tissue of the
patient.
[0169] Treatment subsystem 800 can further include an energy delivery unit,
EDU 810
which provides energy to the one or more treatment elements 870. EDU 810 can
provide one
or more forms of energy selected from the group consisting of: electromagnetic
energy such
as RF energy and/or microwave energy; thermal energy such as heat energy
and/or cryogenic
energy; light energy such as laser light energy; sound energy such as
ultrasound energy;
chemical energy; mechanical energy; and combinations of these. Alternatively
or
additionally, EDU 810 can provide an agent to one or more treatment elements
870, such as
when treatment elements 870 comprise an agent delivery element as described
hereabove.
[0170] In some embodiments, treatment subsystem 800, treatment catheter
850, and/or
EDU 810 are of similar construction and arrangement to the similar components
described in
applicant's co-pending United States Patent Application Serial Number
14/422,941, titled
"CATHETER, SYSTEM AND METHODS OF MEDICAL USES OF SAME, INCLUDING
DIAGNOSTIC AND TREATMENT USES FOR THE HEART", filed February 20, 2015, the
content of which is incorporated herein by reference in its entirety.
[0171] In some embodiments, treatment subsystem 800 is used to treat the
patient based
on the diagnostic results 1100 (e.g. results which are based on complexity
assessment
provided by algorithm 600). For example, ablation energy can be delivered to
the heart wall
at one or more locations (e.g. one or more vertices described hereabove),
where the
complexity assessment determines if a complexity level for a location exceeds
(e.g. is above)
a threshold, and therapy is delivered to all locations where the threshold is
exceeded. In some
embodiments, one vertex is selected for ablation, in a region of multiple
vertices, where
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system 100 (e.g. via algorithm 600) determines a maximum complexity level to
exist (e.g. a
"local maximum" is ablated), and where the maximum complexity level can be an
absolute
maximum or a relative maximum.
[0172] In some embodiments, therapy provided by system 100 (e.g. ablation
energy
delivered to one or more vertices) is delivered in a closed-loop fashion, such
as in a manual
(clinician driven), automated (e.g. system 100 driven), and/or semi-automated
(e.g. combined
clinician and system 100 driven) mode. Closed-loop operation can include:
manipulation of
treatment element 870 to a location to be treated (e.g. via clinician
manipulated and/or system
100 robotically manipulated treatment device 850); and/or setting of energy
level to be
delivered.
[0173] Referring now to Fig. 2A and 2B, a visual representation of a data
structure and
a portion of the data structure are illustrated, respectively, consistent with
the present
inventive concepts. System 100, as describe hereabove, can measure and record
the size and
shape of a heart chamber HC, for example to provide an approximation of the
shape of
chamber HC at diastole. In some embodiments, system 100 measures chamber HC
via
ultrasound transducers 12b of catheter 10, and the measurement information can
then be
processed by processor 26, and recorded as a set of information defined by a
data structure as
described herebelow. Alternatively or additionally, system 100 can include
other imaging
elements and/or devices to provide cardiac anatomy information to processor
26. The
processed information provided by processor 26 (e.g. anatomic data 110) can be
stored as a
set of nodes, each node comprising a vertex V of a geometric representation of
the anatomy,
for example a triangular mesh representing the chamber HC, mesh 80 shown. Each
vertex V
in mesh 80 is connected to its neighboring vertices V by edges E, edges of the
polygons (e.g.
triangles) that define mesh 80.
[0174] Any vertex V can be defined as a central vertex CV. For a central
vertex CV, a
"neighborhood" of surrounding vertices V can be defined ("neighborhood" or
"neighborhood
of vertices" herein). For example, a neighborhood of first neighbors can
comprise central
vertex CV as well as all vertices V connected by a single edge E to central
vertex CV.
Furthermore, a neighborhood of second neighbors can further comprise all
vertices V
connected by a single edge E to any of the first neighbors of central vertex
CV. A two-edge-
connected neighborhood is illustrated in Fig. 2B. A multiple-edge-connected
neighborhood
can be defined by the number of edges from central vertex CV (e.g. in a five-
edge-connected
neighborhood, each included vertex V is within five edges of central vertex
CV). As used
herein, a "border vertex" can be defined as a vertex V included within the
neighborhood, that
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is located at a particular number of edges from the central vertex (i.e. the
number of edges
that defines the size of the neighborhood). A "boundary vertex" can be defined
as a vertex V
one-edge-connected to a border vertex, but not included within the
neighborhood (a vertex
that is within one edge-connection of a border vertex but not within the
neighborhood).
[0175] For each vertex V, information corresponding to its anatomic
location can be
recorded and stored by system 100. For example, for an instance in time, bio-
potential data
measured by system 100 can be processed and recorded as a set of values, each
corresponding to a vertex V, for that instance in time, (a "frame" of data).
System 100 can be
configured to record bio-potential or other data for an extended period (e.g.
100ms to 500ms),
represented by multiple sequential frames, each containing time related
information
correlating to the vertices V of mesh 80.
[0176] In some embodiments, each frame contains not only the bio-potential
data
corresponding to each vertex V but also other calculated and/or measured
information
corresponding to each vertex V. For example, system 100 can include one or
more
algorithms, as described herebelow, classifying each vertex V for each frame
(e.g.
classification information that is stored for each frame). Additionally or
alternatively, system
100 can "pre-process" recorded bio-potential data, and save the results of the
processing for
each frame. For example, for each vertex V of each frame, BIO processor 36 can
determine
if for that instance in time, a vertex is "active" (e.g. along the leading
edge of a depolarizing
conducting wave propagating through the cardiac tissue), or not. In some
embodiments, a
binary active or not-active "flag" (i.e. a binary yes/no data point) decreases
the processing
time for an algorithm. Additionally or alternatively, for each vertex V of
each frame, the
current activation status and the activation history can be stored (e.g. a
history representing if
the vertex is active, or had been active within a predetermined time period
such as within the
previous 100ms). In these embodiments, the length of the history recorded for
each vertex,
and/or the resolution of that recording, can be selected (e.g. pre-selected by
the manufacturers
of system 100, and/or selected by an operator) to balance the speed of one or
more algorithms
of system 100 versus the overall resolution of the resultant calculations. As
used herein,
activations "within" a neighborhood can include all activations recorded for
each vertex V
within the neighborhood for all frames (e.g. for the length of a recording),
or it can include
only the activations within a time window (e.g. a rolling time window as
described herebelow
in reference to Fig. 8) of the activation of central vertex CV of the
neighborhood, for example
within +/-100ms of the activation of central vertex CV. In some embodiments,
an activation
is only included in the set of neighborhood activations if the activation is
considered within a
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"minimum and maximum speed estimation", as described herebelow in reference to
Fig. 4.
For example, if an activation of a border vertex occurs within 100ms of the
activation of
central vertex CV, but the physical distance between the points on the tissue
represented by
the two vertices is "too long or too short", such that the computed speed is
not within the
maximum or minimum speed (e.g. an estimated range of physiological conduction
of tissue),
the activation is excluded.
[0177] In some embodiments, system 100 is constructed and arranged to
perform one or I
more of the algorithms described herein on a portion of mesh 80. For example,
a portion of
mesh 80 representing tissue proximate the pulmonary veins can be analyzed
(e.g. by FA
algorithm 500 described herebelow) to identify focal activity, as focal
activity near the
pulmonary veins has been associated with patients having an arrhythmia such as
AF.
Additionally or alternatively, one or more algorithms of system 100 can
comprise a bias
and/or one or more thresholds of an algorithm can be adjusted (e.g. biased)
based on the
anatomic tissue being analyzed. For example, FA algorithm 500 can be biased
towards
identifying focal activity proximate the pulmonary veins.
[0178] Referring now to Fig. 3, a schematic view of an algorithm for
performing a
complexity assessment is illustrated, consistent with the present inventive
concepts.
Algorithm 600 shown can be included in one or more portions of system 100
described
hereabove, such as when console 20 comprises algorithm 600. Algorithm 600 is
configured
to perform a complexity assessment based on recorded bio-potential data, such
as bio-
potential data recorded by electrodes 12a of catheter 10. Algorithm 600 can
perform a
complexity assessment based on, as shown in Fig. 3, electrical activity data
120 (e.g.
activation timing data 121) and/or anatomic data 110.
[0179] In Step 610, for each frame (as described hereabove) the active
vertices (also as
defined hereabove) of the anatomic data 110 are determined, and activation
propagation data
is calculated. Step 610 can use an optical flow algorithm (e.g. Horn-Schunck)
or other 2D or
3D image-based analysis algorithm to calculate the activation propagation data
at each
location.
[0180] In Step 620, an analysis of the activation propagation data from
frame to frame is
performed. In this analysis, patterns can be identified, such as rotational
patterns, localized
irregular patterns, focal activation patterns, and/or other normal or abnormal
electrical
activity patterns. Patterns can be identified using one or more pattern
detection algorithms,
such as algorithms 300, 400, and/or 500 described herebelow.
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[0181] In Step 630, a complexity assessment is performed, such as to
produce diagnostic
results 1100. Diagnostic results 1100 can be provided to a clinician, such as
to determine a
therapy to be administered to the patient (e.g. one or more cardiac tissue
locations to perform
a cardiac ablation procedure, such as using treatment subsystem 800 described
hereabove in
reference to Fig. 1). In some embodiments, algorithm 600 further includes a
complexity
algorithm 650 configured to process and/or assess diagnostic results 1100, as
described
herebelow in reference to Fig. 3A.
[0182] Diagnostic results 1100 can comprise scalar values, for example a
scalar value
assigned to each vertex assessed, representing the "level" of complexity, as
calculated over a
time period (e.g. time periods TP described herebelow). Additionally or
alternatively,
diagnostic results 1100 can comprise time varying values, for example a binary
value
assigned to each vertex assessed, representing "complex" or "not", calculated
for several
instances in time (e.g. time period TP1 described herebelow). In some
embodiments, binary,
time varying values is summed, or otherwise combined, to determine a scalar
value of the
level of complexity over a longer time period TP (e.g. time period TP2, TP3,
or TP4
described herebelow). In some embodiments, binary and/or scalar values are
assigned
"persistently" to a vertex over subsequent frames of data, for example a
binary "yes" can be
assigned persistently to a vertex for two, three, or more subsequent frames,
potentially
overriding a binary "no" from the calculated results. Additionally, repeated
positive
indicators can be assigned a longer persistence, for example three binary
"yes" frames (for a
single vertex) can be assigned 5 additional "yes" values (8 total, assuming
all relevant
subsequent values are "no"), while a single binary "yes" frame can be assigned
only 2
additional "yes" values (3 total).
[0183] In some embodiments, electrical activity data 120a is recorded (e.g.
recorded by
electrodes 12a), from at least 10, or at least 48, or at least 64 heart wall
locations (e.g. in a
contact-mapping procedure). In these embodiments, the vertices determined by
system 100
can include the recording locations and/or other heart wall locations. In
these embodiments,
the electrical activity data can be recorded simultaneously or sequentially.
[0184] In some embodiments, electrical activity data 120a is recorded (e.g.
recorded by
electrodes 12a), from at least 10, or at least 48, or at least 64 locations
within a heart chamber
(e.g. contacting and/or non-contacting the heart wall). In these embodiments,
the vertices
determined by system 100 can include the heart-wall based recording locations,
and/or other
heart wall locations. In these embodiments, the electrical activity data 120
can be recorded
simultaneously or sequentially.
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[0185] Referring additionally to Fig. 3A, complexity algorithm 650 can be
configured
to process and/or assess diagnostic results 1100 as produced in STEP 630, as
described
hereabove in reference to Fig. 3. In STEP 6510, algorithm 650 can assess the
type and
consistency of each complex activation pattern as identified in diagnostic
results 1100. In
STEPs 6520 and 6530, algorithm 650 can assess the proximity (e.g. in space)
and/or the
relationship (e.g. in time) between each complex activation pattern, and can
then determine if
an identified complex activation pattern is part of a "macro-level" complexity
activation
pattern. In STEP 6540, algorithm 650 can apply a computation method to assess
and/or
predict a probabilistic outcome of delivering therapy to a location of a macro-
level complex
activation pattern. In some embodiments, the computational method comprises
data
analytics/statistics techniques, such as classification or categorization, of
electrical activity
using a training data set (e.g. separately acquired data, such as historical
data) and/or a
computationally-optimized fit (e.g. machine learning or predictive analysis,
such as by neural
network or deep learning, cluster analysis).
[0186] STEP 6540 can be configured to provide updated diagnostic results
1100' as
shown, which can include: identification of macro-level complexity; a
prioritization of
therapeutic targets; a probabilistic and/or predictive therapeutic strategy;
one or more
modifications to diagnostic results 1100; and combinations of these. In some
embodiments,
the probabilistic outcome of delivering therapy is determined, or otherwise
provided, through
the use of machine learning, as described in applicant's co-pending United
States Patent
Provisional Application Serial Number 62/668,659, titled "CARDIAC INFORMATION
PROCESSING SYSTEM", filed May 8, 2018, the content of which is incorporated
herein by
reference in its entirety for all purposes. In some embodiments, the
predictive therapeutic
strategy may be to cause the current rhythm to transition to a less complex
rhythm (e.g. to
transition from atrial fibrillation to atrial tachycardia), such as a strategy
determined using
state analysis. The current state of a rhythm can be defined by one or more
complexity
metrics (e.g. cycle length, number of cardiac waves, Shannon entropy, and/or
dominant
frequency). State changes can be estimated for various therapeutic strategies
(e.g. various
ablation locations and/or durations). The therapeutic strategy that is
estimated to change the
rhythm to the least complex state can then be implemented. Complexity
algorithm 650 can
take as an input other patient data (e.g. MRI/CT data, patient health history
data, and/or
previous ablation history data).
[0187] Complexity algorithm 600 can comprise an analysis of recorded
electrical activity
data 120a that is recorded over time periods TP, which can comprise similar or
different
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lengths of time. Each time period TP can represent all or a portion of a
continuous recording
for that time period TP, or all or a portion of multiple recordings that
cumulatively represent
the time period TP. In some embodiments, a time period TP represents two or
more periods
of recording electrical activity as well as the time between recordings. In
some embodiments,
data that has been recorded over a period of time is segmented into multiple
time periods TP
(e.g. multiple time periods of the same duration), and a complexity assessment
is calculated
over each time period TP. The complexity assessment can then be displayed to a
user in a
video like format (e.g. displayed on display 27a, as described herebelow in
reference to Fig.
8). In some embodiments, each time period TP (e.g. time period TP2 described
herebelow)
comprises a sufficiently long time period TP, such that a user can reasonably
perceive the
displayed information in a "real rate" fashion (e.g. the information is
displayed at the same
rate that it occurred). In these embodiments, the displayed information can be
presented in a
"real time" fashion (e.g. information is displayed as it occurs, with minimal
delays due to
processing by system 100). Alternatively or additionally, the time period TP
can comprise a
sufficiently short time period (e.g. time period TP1 described herebelow),
such that a user
cannot reasonably perceive the displayed information when displayed in a real
rate fashion.
In these embodiments, a rolling "average" of data can be displayed at a real
rate, and/or the
data can be replayed in a frame by frame or other slow-motion fashion such
that the user can
reasonably perceive the data. Additionally or alternatively, various methods
of displaying
accumulated, summed, averaged, or persistent data can be implemented to
provide the user a
perceivable time-dependent representation of the calculated data. Furthermore,
each time
period TP (e.g. TP3 and/or TP4 described herebelow) can comprise an extended
time period,
and/or a time period spanning two or more discrete recordings, and a time
compressed (e.g.
time-lapse) data set can be displayed to the user. Playback and other data
display modes are
described in detail herebelow in reference to Fig. 8.
[0188] In some embodiments, a time period TP1 comprises a relatively short
time period,
such as a period in which between 1-10 activations occur in the cardiac tissue
being assessed
(e.g. as represented by a set of vertices as described herein).
Correspondingly, TP1 can
comprise a duration of between 0.3ms and 2000ms, such as a time period of
approximately
150ms. In some embodiments, catheter 10 comprises a contact mapping catheter
(e.g. a
"roving" contact mapping catheter, configured to record electrical activity
data 120a via
electrodes 12a only from a single discrete portion of the heart chamber at one
time). In these
embodiments, time period TP1 can approximate the total recording time at a
single discrete
portion of the heart chamber, a "visit". A subsequent time period TP1 can
approximate a
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subsequent visit to the same discrete portion of the heart chamber or a
different portion. In
these embodiments, two, three, or more recordings, each comprising a time
period
approximately equal to TP1 can be combined to create a more complete data set
of recorded
electrical activity. The two, three, or more recordings can be combined
spatially, based on
the portion of the heart chamber recorded, as well as temporally, based on the
heart cycle
information, as is known in the art of contact cardiac mapping. In some
embodiments,
catheter 10 comprises a mapping catheter (e.g. a basket catheter), configured
to record
electrical activity data 120a via electrodes 12a from a distributed set of
locations all around
the chamber where the electrode locations are intended to be in contact, or
near-contact, with
the cardiac wall. In some embodiments, catheter 10 comprises a mapping
catheter (e.g. a
basket catheter), configured to record electrical activity data 120a via
electrodes 12a from a
distributed set of locations offset with the cardiac wall.
[0189] In some embodiments, complexity algorithm 600 comprises an analysis
of
electrical activity data 120a that is recorded for a time period TP2 that
includes a moderate
number of electrical activations, such as between 3 and 3000 activations, such
as between 10
and 600 activations, or between 25 and 300 activations. Correspondingly, TP2
can comprise
a duration of between 0.3secs and 500secs, such as a time period between lsec
and 90secs or
between 4secs and 30secs. In some embodiments, time period TP2 represents the
length of a
single data recording, for example a contact and/or non-contact recording of
electrical
activity data 120a within a heart chamber.
[0190] In some embodiments, complexity algorithm 600 is configured to
analyze
electrical activity data 120a that is recorded for a time period TP3 that
includes a large
number of electrical activations, such as between 2,000 and 300,000
activations, such as
between 6,000 and 40,000 activations. Correspondingly, TP3 can comprise a
duration of
between 5 minutes to 8 hours, such as between 15 minutes and 60 minutes. In
some
embodiments, time period TP3 represents the length of several recordings of
acute electrical
activity, for example several recordings taken before, after, and/or
interspersed between loop-
iterations of diagnosis and therapy (e.g. therapy provided by treatment
subsystem 800
described hereabove in reference to Fig. 1).
[0191] In some embodiments, complexity algorithm 600 is configured to
analyze
activations and/or electrical data from measurements made with a regional
focus. A regional
focus can include a region of tissue comprising between approximately 5% and
50% of the
heart chamber surface (e.g. between 5% and 50% of the endocardial surface of
an atrium or
ventricle). The measurements can be made with enough time to capture
characteristics of
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complex conduction representative of the rhythm, such as to capture between
approximately
3 and 3000 activations. In some embodiments, electrode array 12 is
sequentially maneuvered
to different position to form an aggregate map comprising the data from each
position.
[0192] In some embodiments, complexity algorithm 600 comprises an analysis
of
electrical activity data 120a that is recorded for a time period TP4 that
includes a time period
of multiple days, weeks, months, and/or years (e.g. spanning more than one
clinical
diagnostic procedure performed on the patient). In some embodiments, time
period TP4
represents the length of several recordings of electrical activity spanning
more than one
clinical procedure, for example spanning days, weeks, months, or years.
[0193] In some embodiments, complexity algorithm 600 receives additional
patient data
150, such as to include both electrical activity data 120 and patient data 150
in a complexity
analysis, such as is described hereabove in reference to Fig. 1. In some
embodiments,
complexity algorithm 600 includes one or more of algorithms 200, 300, 400,
and/or 500
described herebelow, each of which can include an assessment of complexity
that is based on
electrical activity data 120, anatomic data 110, and/or additional patient
data 150.
[0194] Referring now to Fig. 4, a schematic view of an algorithm for
determining
conduction velocity data is illustrated, consistent with the present inventive
concepts. System
100 can comprise a conduction velocity algorithm, CV algorithm 200, that
analyzes anatomic
data, data 110 shown, and activation timing data, data 121 shown. Complexity
algorithm 600
described hereabove can comprise CV algorithm 200. CV algorithm 200 can
comprise one
or more instructions executed by a processor of system 100, for example
processor 26 of
console 20. CV algorithm 200 can process anatomic data 110 and electrical
activity data 120
(e.g. activation timing data 121) to determine the conduction velocity at each
vertex of the
anatomic data 110, for each activation of the associated vertex, as described
herein.
[0195] In some embodiments, CV algorithm 200 computes one or more
components of
the velocity (direction and/or magnitude) at each vertex of anatomic data 110
as a
depolarizing conducting wave passes through the vertex. The conduction
velocity (e.g. the
velocity at each vertex as the depolarizing conductive wave passes through the
vertex) can be
found by determining the spatial gradient of the activation times (c) using
the following
equation:
dx dy dz
V-c = =
GT GT GT
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[0196] Each vertex processed can be considered a "central vertex", and a
small
"neighborhood" composed of vertices and activation times proximate each
central vertex can
be used to estimate the spatial gradient and to find the conduction velocity
at the central
vertex. In some embodiments, a method for estimating the spatial gradient of
activation times
for a vertex given a small neighborhood and the positions of the vertices in a
small
neighborhood comprises fitting the activation times in the neighborhood to a
function (e.g. a
polynomial function) of the positions of the vertices. In some embodiments, a
polynomial
surface fitting method is used.
[0197] CV algorithm 200 can process each frame of anatomical data 110 and
electrical
activity data 120a recorded by system 100. In Steps 210-250 described
herebelow,
processing of a single frame of data is performed. Multiple frames can be
processed through
the repeating of Steps 210-250 on subsequent frames.
[0198] In Step 210, a set of active vertices is determined using anatomic
data 110 and
electrical activity data 120 (e.g. activation timing data 121).
[0199] In Step 220, for each active vertex of the anatomy (for the current
frame), a
neighborhood of vertices can be defined around that vertex (e.g. a central
vertex of that
neighborhood). In some embodiments, multiple-edge-connected (e.g. five)
neighbors are
used to define a neighborhood covering approximately 200mm2-315mm2 of the
anatomical
surface with 60-120 vertices included in the neighborhood (for example,
neighborhoods as
described hereabove in reference to Fig. 2B). Within the neighborhood defined
by the
multiple-edge-connected neighbors, all activation times T are found that are
within a
particular minimum speed estimation (e.g. a minimum speed estimation of
approximately
0.3m/s), where speed is estimated as:
S 11Pcenter(x,y,z)-Pi(x,y,z)11
peed =
ITcenter vertex¨ Ti I
where P is the position of a vertex.
[0200] The principal components of this neighborhood are then determined by
creating a
matrix of all the vertices positions in the neighbor with the mean removed.
Singular value
decomposition (SVD) of the matrix of vertex positions can be used to determine
the three
singular vectors for the local neighborhood, which correspond to the principal
components of
the neighborhood. The positions of vertices in the neighborhood are
transformed into a basis
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defined by the neighborhood's principal components by multiplying the singular
vectors with
the positions of each vertex in the neighborhood Poriginal, where:
Poriginal * SingularVectors = P
- prinipal=
[0201] After the transform, the neighborhood can be described by spatial
variables
(ui, vi, ki), where (ui, vi, ki) is the amount of the first, second and third
principal component,
respectively, used to describe the position of the ith vertex as shown below:
Principal Component Transform
neighborhood(xi, yi, zi, Ti) ________________ > neighborhood(ui, vi, ki, TO.
[0202] In some embodiments, an optional Step 230 is performed. In Step 230,
the
singular vector with the smallest singular value in P
- prinipal is removed resulting in converting
a 3-dimensional domain to a 2-dimensional planar domain, as performed using
the following
function:
Remove singular vector with smallest singular value
neighborhood(xi, yi, zi, _________________________ > neighborhood(ui, vi,
Ti).
[0203] The resulting plane is the best fit plane of the 3-dimensional
positions of the
vertices converted to a 2-dimensional plane. The 3-dimensional to 2-
dimensional transform
can be performed to ensure that the computed conduction velocity is tangent to
the surface
anatomy, and/or to reduce the dimensionality of the polynomial surface fitting
performed in
subsequent following steps, such as those described herebelow.
[0204] In Step 240, a function (e.g. a best fit cubic polynomial surface
function, T) is
used to describe the local activation times, Ti, of the neighborhood as a
function of position
(ui, vi), for example such that T(ui, vi) Ti as shown below:
T(u, = a0u3 + a8v3 + a7112v + a6uv2 + a5u2 + a4v2 + a311v + a2u + aiv + a0.
[0205] Given a set of [u,v] = T, the following matrix can be constructed to
solve for the
coefficients A.
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[
/14 v uNi um 14 17,2_ uivi ui vi 1 -a9 1
T.,-
i , i
?,, v?, uv, uNvA 114 14, uNvN UN vu 1j _act Tn-
Aa' =
[0206] The above can be solved with a least squares analysis. Singular
value decomposition
can be applied to matrix A: A = USVT, from which the pseudo inverse of A can
be calculated,
[
which in turn can be used to calculate the coefficients:
= A+tf = (VS-1UTO
[0207] In Step 250, the conduction velocity can be solved for by
analytically finding the
derivatives of the surface (e.g. the polynomial surface T), as shown below:
- dT
du
-du r_d'I\ 2 4_ (CIT) 2
V_ [VI _ dT ,:lu.) ldv)
Vv] ¨ dv dT
-dT dv
01)2 _L (dT)2
-ldu) -I- WO -
[0208] The conduction velocity can then be normalized to create unit
vectors, such as by
using the following equation:
-µTT Vunit
v unit = ¨
III/II
[0209] Via the preceding steps, algorithm 200 produces a set of conduction
velocity data,
data 122 shown, which is based on the anatomic data 110 and activation timing
data 121.
[0210] In some embodiments, the conduction velocity data 122 can be
represented on the
anatomical surface (e.g. via display 27a of system 100) by transforming the
resulting
conduction velocity unit vectors back into the original coordinate system
(e.g. the coordinate
system of anatomic data 110), such as by using the following equation:
¨ ¨
Vunitu,v Coordinates * Singular VectorsT = V
uny,z coordinates'
1
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[0211] For each activation (e.g. each activation of each central vertex for
each frame), the
conduction velocity can be represented 2-dimensionally and/or 3-dimensionally,
such as by
using the following equation:
Undo Principal Component Transform
131 31 Vvii Principal Component Transform and Nxi, V31,Vzil
Remove Singular Vector
with Smallest Singular Value
[0212] Referring now to Fig. 5, a schematic view of an algorithm for
determining
localized rotational activity is illustrated, consistent with the present
inventive concepts.
System 100 can include an algorithm for determining localized rotational
activity, LRA
algorithm 300. Complexity algorithm 600 described hereabove can comprise LRA
algorithm
300. LRA algorithm 300 can be configured to determine the angular change in
conduction
velocity relative to a central vertex. In atrial fibrillation (AF) and other
arrhythmia patients,
cardiac electrical activity can manifest as rotors (e.g. rotational electrical
activity around a
central obstacle). Such rotational activity has long been thought to have a
prominent role in
the maintenance of a cardiac arrhythmia such as AF (e.g. rotational activity
is associated with
causing and/or perpetuating these undesired conditions).
[0213] In some embodiments, LRA algorithm 300 is used to process each frame
of
anatomical data 110 and electrical activity data 120 (e.g. activation timing
data 121) collected
by system 100. In Steps 310-360 described herebelow, processing of a single
frame of data is
performed. Multiple frames can be processed through the repeating of Steps 310-
360 on
subsequent frames. In some embodiments, LRA algorithm 300 also includes
conduction
velocity data 122 in its analysis. Alternatively or additionally, LRA
algorithm 300 can be
configured to determine conduction velocity data 122, such as when LRA
algorithm 300 is
configured similar to CV algorithm 200.
[0214] In Step 310, a set of active vertices is determined using anatomic
data 110 and
electrical activation data 120 (e.g. activation timing data 121).
[0215] In Step 320, for each active vertex of the anatomy (for the current
frame), a
neighborhood of vertices can be defined around that vertex (e.g. a central
vertex of that
neighborhood). For each neighborhood, a ring of vertices around the central
vertex can be
defined by the boundary vertices of the neighborhood, as shown in Figs. 5A-B.
[0216] In Step 330, for each neighborhood, the activation times and
conduction velocities
for the vertices in the neighborhood can be grouped (e.g. binned). For each
neighborhood, all
activation times that are within a particular maximum speed estimation (e.g. a
maximum
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speed estimation of approximately 0.05m/s) can define (e.g. limit) the set of
activations to be
grouped. In some embodiments, only activation times that are reachable from a
group's
center vertex activation with a given maximum speed (e.g. 0.05 m/s), are
included within the
group. The activations in each neighborhood can be grouped as shown in Fig.
5B. In some
embodiments, the average activation timing data 121 and/or the average
conduction velocity
data 122, for all activations within a group, is assigned to a boundary
vertex, also as shown in
Fig. 5B.
[0217] In Step 340, vertices with a linear trend of activation times (e.g.
an increasing or
decreasing trend) around the outer ring of vertices are identified. For
example, a linear fit
with an R2? 0.7 can be identified as a trend. Fig. 5D shows a trend line of
activation times.
[0218] In Step 350, the total angular change between the average conduction
velocities
assigned to the first and last vertices of the linear trend identified in Step
340, is determined.
Fig. 5E shows the conduction velocities of an identified linear trend that
have been translated
to an origin point, 0,0. Fig. 5E graphically illustrates the total angular
change between
average conduction velocities as described hereabove.
[0219] In Step 360, LRA algorithm 300 classifies a central vertex as
"rotational" if the
linear trend identified in Step 340 exceeds a threshold (e.g. an operator-
defined threshold)
and/or if the total angular change identified in Step 350 exceeds a threshold.
[0220] LRA algorithm 300 produces a set of data (e.g. creates new data
and/or modifies
existing data), classified activation data 140 (e.g. data that has been
filtered, categorized,
identified and/or otherwise classified to identify activations as being
rotational in nature).
[0221] Referring now to Fig. 5A, a graphical representation of anatomic
data 110 is
illustrated, including a neighborhood of vertices defined by an outer ring of
vertices.
[0222] Referring now to Fig. 5B, a simplified representation of a
neighborhood of
vertices is illustrated, including an outer ring of vertices positioned about
a central vertex. In
some embodiments, activations within a neighborhood is segmented, or binned,
and
subsequently averaged. The average values can be assigned to a single vertex,
for example a
border vertex within the segment. For example, all activations within an area
of the
neighborhood represented by shaded portion 51 can be averaged and "assigned"
to vertex Vi.
In some embodiments, the binning is performed to limit the effect of noise on
subsequent
calculations performed on the data. In some embodiments, the size of segment
Si is chosen
to increase the resolution of system 100 (e.g. smaller segments) or to
decrease subsequent
calculation time (e.g. larger segments).
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[0223] Referring now to Fig. 5C, a representative anatomy showing an
example
propagating wave rotating about a neighborhood is illustrated, the
neighborhood defined by
an outer ring of vertices positioned around a central vertex. Average
conduction vectors are
also shown from each boundary vertex of the ring.
[0224] Referring now to Fig. 5D, a plot of the activation times in the
outer ring of
vertices of Fig. 5C is illustrated, the activation times plotted against
degrees around the
central vertex. The points on the plot show a set of vertices in the ring with
a linear trend, as
described hereabove. In the data shown in Fig. 5D, the trend extends from
approximately 200
degrees to approximately 375 degrees, indicative of a cardiac wave that has
propagated 175
degrees around the central vertex.
[0225] Referring now to Fig. 5E, a graph of conduction velocity vectors
associated with
Fig. 5C is illustrated, the vectors translated to a point 0,0. The conduction
velocity change
around the central vertex can be determined by summing up the angles between
the
sequential conduction velocity vectors. For this example, the conduction
velocity vectors of
the illustrated data, represented by angle a, sum up to 155 degrees.
[0226] Referring now to Fig. 6, a schematic view of an algorithm for
determining
localized irregular activity is illustrated, consistent with the present
inventive concepts.
System 100 can include an algorithm for determining localized irregular
activity, LIA
algorithm 400. Complexity algorithm 600 described hereabove can comprise LIA
algorithm
400. LIA algorithm 400 can be configured to determine the angle between the
direction of
conduction approaching a central vertex and the direction of conduction
departing a central
vertex. Irregular activity, such as notable fractionation, irregular reentrant
type activity,
and/or disorganized conduction, has long been thought to have a prominent role
in the
maintenance of cardiac arrhythmia, including AF.
[0227] In some embodiments, LIA algorithm 400 is used to process each frame
of
anatomical data 110 and electrical activity data 120 (e.g. activation timing
data 121) collected
by system 100. In Steps 410-460 described herebelow, processing of a single
frame of data is
performed. Multiple frames can be processed through the repeating of Steps 410-
460 on
subsequent frames. In some embodiments, LIA algorithm 400 also includes
conduction
velocity data 122 in its analysis. Alternatively or additionally, LIA
algorithm 400 can be
configured to determine conduction velocity data 122, such as when LIA
algorithm 400 is
configured similar to CV algorithm 200.
[0228] In Step 410, a set of active vertices is determined using anatomic
data 110 and
activation timing data 121.
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[0229] In Step 420, for each active vertex of the anatomy (for the current
frame), a
neighborhood of vertices can be defined around that vertex (e.g. a central
vertex of that
neighborhood). For each neighborhood, a ring of vertices around the central
vertex can be
defined by the boundary vertices of the neighborhood, such as is shown in Fig.
5A.
[0230] In Step 430, for each neighborhood, LIA algorithm 400 can be
configured to
determine the mean conduction velocity direction for all activations within
the neighborhood
that: have an earlier activation time than the central vertex's activation
time (within a
maximum conduction speed, such as a maximum between 0.3 m/s - 3m/s); and have
a
conduction velocity direction pointing towards the central vertex. In some
embodiments,
only a subset of these activations is included in the calculation of the mean
conduction
r:
Velocity direction.
[0231] In Step 440, for each neighborhood, LIA algorithm 400 can be
configured to
determine the mean conduction velocity direction for all activations within
the neighborhood
that: have a later activation time than the central vertex's activation time
(within a maximum
conduction speed, such as a maximum between 0.3 m/s - 3m/s); and have a
conduction
velocity direction pointing away from the central vertex. In some embodiments,
only a
subset of these activations is included in the calculation of the mean
conduction velocity
direction.
[0232] In Step 450, LIA algorithm 400 determines the angle between the mean
conduction velocity direction entering the neighborhood, and the mean
conduction velocity
direction leaving the neighborhood.
[0233] In Step 460, LIA algorithm 400 classifies a central vertex as
"irregular" if the
angle determined in Step 450 exceeds a threshold (e.g. an operator-defined
threshold). LIA
algorithm 400 produces a set of data (e.g. creates new data and/or modifies
existing data),
classified activation data 140 (e.g. data that has been filtered, categorized,
identified and/or
otherwise classified to identify activation as being irregular in nature). In
some
embodiments, a vertex can be previously classified as rotational (e.g. when
LRA algorithm
300 has been performed previously) and LIA algorithm 400 does not reclassify
or
additionally classify the vertex as irregular. Alternatively or additionally,
classified
activation data 140 can allow multiple classifications for each vertex. In
these embodiments,
system 100 can be configured to apply a weighting factor, or otherwise
prioritize certain
classifications, for example a rotational classification can be considered
more important than
an irregular classification.
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[0234] Referring now to Fig. 6A, an example of a propagation wave showing
irregular
activation is illustrated, consistent with the present inventive concepts.
Fig. 6A shows a
propagation wave PW1 entering a small region, dot CV. The conduction
velocities from
PW1 can be averaged to determine a mean conduction velocity direction entering
the region
CV. Fig. 6A also shows a propagation wave PW2 leaving the region CV. The
conduction
velocities from PW2 can be averaged to determine a mean conduction velocity
direction
leaving the region CV. LIA algorithm 400 can be configured to determine the
angle 13
between the direction of conduction approaching CV and the direction of
conduction
departing CV (as described hereabove). LIA algorithm 400 can classify the
central vertex at
its activation time as irregular if the angle exceeds a threshold (e.g. a user
defined threshold,
also as described hereabove).
[0235] Referring now to Fig. 7, a schematic view of an algorithm for
determining focal
activation is illustrated, consistent with the present inventive concepts.
System 100 can
include an algorithm for determining focal activation (also referred to as
focal activity), FA
algorithm 500. Complexity algorithm 600 described hereabove can comprise FA
algorithm
500. FA algorithm 500 can be configured to determine whether an activation at
a vertex
originated from a previous cardiac wavefront, or whether activation
spontaneously started
from the vertex (known as focal activation). Focal activation is detected at a
vertex if that
activation is earlier than the activation of neighboring vertices, and
conduction spreads
outward from the vertex. Focal activity from the pulmonary veins has been
shown to have a
pivotal role in maintaining paroxysmal AF. More generally, focal activity is
thought to also
have a prominent role in the maintenance of cardiac arrhythmia including AF.
[0236] In some embodiments, FA algorithm 500 is used to process each frame
of
anatomical data 110 and electrical activity data 120 (e.g. activation timing
data 121) collected
by system 100. In Steps 510-560 described herebelow, processing of a single
frame of data is
performed. Multiple frames can be processed through the repeating of Steps 510-
560 on
subsequent frames. In some embodiments, FA algorithm 500 also includes
conduction
velocity data 122 in its analysis. Alternatively or additionally, FA algorithm
500 can be
configured to determine conduction velocity data 122, such as when FA
algorithm 500 is
configured similar to CV algorithm 200. In some embodiments, FA algorithm 500
includes
conduction divergence data 123 in its analysis, as defined herebelow.
Conduction divergence
data 123 can be produced by FA algorithm 500 and/or another algorithm of
system 100 (e.g.
produced prior to the application of FA algorithm 500).
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[0237] In some embodiments, conduction divergence data 123 comprises the
divergence
of conduction velocity from each vertex of anatomic data 110. Divergence of
the conduction
velocity fields can be defined as:
clV clV
div V = ¨+
du dv
where V+ is the normalized conduction velocity. Similar to the estimation of
the conduction
velocity, the divergence of the conduction velocities can be estimated by
fitting Vu and Vv in
a small region to a function (e.g. a 3rd order polynomial) of position, such
that,
Vu = F(u, v) and Vv = G (u, v).
The divergence of the vector field can then be computed as:
dF dG
div V = ¨ + ¨ .
du dv
[0238] For every activation of every vertex, if it is determined that the
divergence of the
conduction velocities has a positive value that exceeds a threshold, the
vertex is classified as
"well-defined" in conduction divergence data 123. In some embodiments, if half
of the
vertices within a multiple-edge-connected (e.g. five) neighborhood have a
conduction
velocity within the minimum conduction velocity range, the divergence is
classified as well-
defined. A positive divergence threshold of 0.05 can be used.
[0239] In Step 510, a set of active vertices is determined using anatomic
data 110 and
activation timing data 121.
[0240] In Step 520, a set of diverging active vertices is identified, from
the set of active
vertices determined in Step 510.
[0241] In Step 530, for each diverging active vertex, a neighborhood of
vertices is
defined around that vertex (e.g. a central vertex of that neighborhood). For
each
neighborhood, a ring of vertices around the central vertex can be defined by
the boundary
vertices of the neighborhood, as shown in Fig. 5A.
[0242] In Step 540, a set of "border vertices" is defined, the set
containing one-edge-
connected neighbors to each boundary vertex of the neighborhood.
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[0243] In Step 550, the activation time of each border vertex defined in
Step 540 is
determined.
[0244] In Step 560, FA algorithm 500 classifies a central vertex as "focal"
if the
activation time of each of its border vertices is later than the activation
time of the central
vertex. FA algorithm 500 produces a set of data (e.g. creates new data and/or
modifies
existing data), classified activation data 140 (e.g. data that has been
filtered, categorized,
identified and/or otherwise classified to identify activation as being focal
in nature). In some
embodiments, a vertex can be previously classified as rotational and/or
irregular (e.g. when
LRA algorithm 300 and/or LIA algorithm 400 has been performed previously) and
FA
algorithm 500 does not reclassify or additionally classify the vertex as
focal. Alternatively or
additionally, classified activation data 140 can allow multiple
classifications for each vertex.
In these embodiments, system 100 can be configured to apply a weighting
factor, or
otherwise prioritize certain classifications (e.g. as described hereabove),
for example a
rotational classification can be considered more important than an irregular
and/or focal
classification.
[0245] Referring now to Figs. 7A and 7B, a representative anatomy showing
focal
activation and a representative anatomy showing focal and passive activation
are illustrated,
respectively, consistent with the present inventive concepts. As shown in Fig.
7A, dot CV
shows the current vertex being evaluated. Border vertices BV are shown
surrounding a
propagation wavefront PW3 that extends from dot CV. As shown in Fig. 7B, dot
CV1 shows
a first vertex, and dot CV2 shows a second vertex. Zoom window (i) of Fig. 7B
shows the
neighborhood of vertices about CV1 and zoom window (ii) of Fig. 7B shows the
neighborhood of vertices about CV2. In the zoom windows of Fig. 7B, the
neighborhoods
are shown projected to a plan and interpolated to a regular grid. As described
hereabove,
complexity algorithm 600 can comprise a supervised learning algorithm, such as
a learning
algorithm that has been trained on a properly labelled training set. The
neighborhood of the
central region (e.g. the region about a vertex CV) can be interpolated into a
nxm regular grid,
such that each value of the grid point contains the activation time, as shown
in zoom
windows (i) and (ii) in Fig. 7B. Temporal information can be added by
concatenating several
images together. Once the activation times are on a regular grid, learning
algorithms ( e.g.
feedforward neural networks, convoluted neural networks, and/or support vector
machines)
can be trained on a large patient set to identify the conduction patterns of
interest given an
image of the conduction pattern. After the activation time data is evaluated
for conduction
patterns of interest while transformed into the image space, the labelled
output can be put
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back and displayed (e.g. in 3D anatomical space). In some embodiments,
complexity
algorithm 600 can be configured to identify electrical patterns selected from
the group
consisting of: LIA; LRA; focal; slow conduction velocity; isthmus-like
conduction; figure of
8's conduction; loop conduction, such as double, triple, or multi-loop
conduction; pivoting
re-entry; and combinations of these. For example, as shown in zoom (i) of Fig.
7B, focal
conduction is illustrated, such as focal conduction that has been identified
as a region of
interest by algorithm 600. As shown in zoom (ii) of Fig. 7B, passive
conduction is
illustrated, such as passive conduction that has been identified as a region
of "non-interest"
by algorithm 600.
[0246] Referring now to Fig. 8, an embodiment of a display on which cardiac
data (e.g.
activation and/or other bio-potential and/or anatomic data) can be rendered is
illustrated,
consistent with the present inventive concepts. The cardiac data can comprise
a series of
frames of data that can be dynamically displayed as a function of time.
Display 1400 of FIG.
8 can be generated using the same processors, modules, and databases described
above for
rendering other displays, such as display 27a of Fig. 1. In some embodiments,
system 100
and/or display 1400 can be of similar construction and arrangement as displays
described in
applicant's co-pending International PCT Patent Application Serial Number
PCT/US2017/030915, titled "CARDIAC INFORMATION DYNAMIC DISPLAY SYSTEM
AND METHOD", filed May 3, 2017, the content of which is incorporated herein by
reference in its entirety for all purposes.
[0247] Within a main cardiac information display window or area, window
1405 (e.g. a
portion of display 1400), a digital model of cardiac anatomy 1402 is shown
with cardiac
activation data superimposed or overlaid thereon. In this embodiment, the
cardiac activation
data is rendered, with an activation status indicated by a series of colors
superimposed on the
digital cardiac model 1402.
[0248] Display 1400 can simultaneously display two or more unique graphical
indicia
representing different physiological parameters of one or more portions of the
heart, as
represented by the digital cardiac model 1402 being displayed. The various
graphical indicia
used to represent these physiologic parameters can be selected from the group
consisting of:
color; a color range; a pattern; a symbol; a shape; an opacity level;
stippling; hue; geometry
of a 2D or 3D object; and combinations of these. The graphical indicia used to
represent the
physiological characteristics can be static and/or dynamic.
[0249] The simultaneous display of multiple physiologic characteristics
(e.g. as
differentiated via the various graphical indicia) can be overlaid on one or
more digital models
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of cardiac anatomy in one or more combinations. Various physiologic
parameters, such as
minimum re-activation time, conduction velocity, number of occurrences the
vorticity
threshold was crossed during a time period, and/or other physiologic
parameters can each be
represented by a unique graphical indicium. A cross-hatch pattern with
discrete levels of
hatch density and/or line thickness can be overlaid on the digital model, such
as to identify
regions falling into different categories of conduction velocity. Surface
spheroids can be
overlaid, centered on nodes with vorticity greater than a threshold, with the
diameter of the
spheroids displayed proportional to the number of occurrences the vorticity
threshold was
crossed during the duration of cardiac activity. Hatch patterns and spheroids
are provided
herein as non-limiting examples of graphical indicia.
[0250] In some embodiments, a display of an electrogram, EGM 1410, is
presented in an
auxiliary cardiac information display window 1415 below the main cardiac
information
display window 1405 displaying the reconstructed heart 1402.
[0251] A set of user-interactive controls, controls 1420, can include a
window width
control 1422 configured to enable a user to set a time duration for display
(e.g. a time
duration for which the calculated data displayed represents), in main cardiac
information
display window 1405, shown here set at 30ms. The window width (time duration)
is
indicated in a semitransparent sliding window, window 1412, which is
superimposed over
EGM 1410. A user-selectable and/or settable display scale, scale 1424, is also
provided,
which can be used for setting a time scale, iSCALL.. Here, tSCALE is set at
3ms. Accordingly,
the horizontal axis of EGM 1410 includes 3ms increments. Play, rewind, and
fast-forward
controls, controls 1426, are also included as shown.
[0252] In some embodiments, diagnostic results 1100 is displayed in main
cardiac
information display window 1405, for example a graphic representation of a
complexity
assessment can be displayed superimposed on reconstructed heart 1402 (e.g. a
complexity
assessment comprising a calculated value of complexity for each vertex of
reconstructed
heart 1402). In these embodiments, window width of window 1412 can indicate
the portion
of recorded data analyzed in the complexity assessment shown (e.g. a time
period for which
the complexity assessment displayed represents). For example, the displayed
complexity
assessment can comprise an average of several complexity assessments
(calculated over two
or more time periods shorter than the within window 1412). The calculation of
various
complexity assessments is described hereabove. The width of window 1412 can be
user
selectable and/or adjustable, such as to produce a complexity assessment which
includes data
from a longer or shorter time period. Two or more complexity assessments can
be displayed
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in a frame by frame fashion (e.g. a movie), where window 1412 "rolls" across
EGM 1410
(e.g. a "rolling window"), indicating for each frame what segment of data was
analyzed.
Alternatively or additionally, a user can position or otherwise adjust window
1412 manually
to generate a complexity assessment for a desired segment of the recorded
data.
[0253] The semitransparent sliding window 1412 is in sync with the cardiac
activation
data shown overlaid on the reconstructed heart 1402. Therefore, the
semitransparent sliding
window 1412 and the cardiac activation data overlaid on the reconstructed
heart 1402 can
dynamically change with respect to a common time scale. The displays are
linked in time,
and change together, since their outputs are based on the same time-dependent
data.
[0254] A set of display mode or layer controls, controls 1428, can be
provided to enable a
user to control at least portions of the display in main window 1405, in
particular to control at
least portions of the display of cardiac activation data on reconstructed
heart 1402. In this
embodiment, separate "buttons" (e.g. electro-mechanical switches, touch screen
icons, and/or
other user-interactive controls) are provided as controls 1428 for selecting
"Color Map,"
Texture Map," "Shade Map," and "Pattern Map" graphical options. In some
embodiments,
one or more of such controls are provided. Not all such controls need be
provided in every
embodiment. In some embodiments, none of the controls 1428 need be provided.
[0255] In FIG. 8, the reconstructed cardiac chamber 1402 is shown with
cardiac
activation data represented as varying colors (e.g. varying greyscale,
responsive to the Color
Map button). For illustration purposes, portions of the reconstructed cardiac
chamber 1402
are shown with a texture map 1404 responsive to the Texture Map button, a
shade map 1406
responsive to the Shade Map button, and a pattern map 1408 responsive to the
Pattern Map
button. That is, in some embodiments, such buttons (or similar controls) are
used to
selectively turn on their respective maps.
[0256] For example, a magnitude-indicating graphic (e.g. a graphic
indicating, roughness,
texture, and the like) which can be uniform, and/or a direction-indicating
graphic (e.g. a grain
such as a wood grain, line segments, spikes, and the like), which can be
directional, can be
overlaid on the surface anatomy to visualize conduction or substrate
characteristics. A z11
height 'roughness' of the magnitude-indicating graphic can be increased or
decreased
proportionally with the degree of the characteristic displayed (e.g. the
magnitude of the
characteristic). Also, the direction of block can be shown with a direction-
indicating graphic,
(e.g. the spikes shown in texture map 1404 of Fig. 8).
[0257] Continuing the above example, shading and/or the use of a distinct
fixed color
palette or gradient (distinct from any other color palette used), such as
grayscale, can be used
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to identify varying degrees of block, such as fixed block, directional block,
and/or functional
block conditions.
[0258] A multi El directional region of activation can be shown with
overlays of different
unidirectional textures or lines, producing a 'hatch' pattern, as shown in
pattern map 1408. A
calculation of an index of fibrosis and/or other physiologic state index
characterizing the
surface/substrate can be displayed with a uniform texture, such as a fine
pattern, such as a
pattern similar in appearance to cement, or a coarser pattern, such as a
pattern similar in
appearance to pebbles. An index of fibrosis or other physiologic state indices
that present an
obstruction or obstacle to the conduction pattern can be determined by a
combination of
velocity, directional uniformity, and/or other conduction pattern
characteristics.
[0259] Incorporating textures, patterns, shading, and the like, on the
surface of the cardiac
chamber 1402, provides a way to provide (e.g. visually provide) more
information in
coordination with other types of cardiac activity information. This
configuration is an
extended implementation of visual 'layers' in the map display that can be used
individually or
in any combination to provide information related to multiple variables
simultaneously, such
as through the use of user-interactive controls 1420.
[0260] In some embodiments, one or more of the classifications of vertices
described
herein are indicated on the reconstructed cardiac chamber 1402. In these
embodiments, the
classification can be indicated as described hereabove, such as with a color
overlay and/or
other graphical indicia. In some embodiments, colored or otherwise
distinguishable "dots"
are used to indicate vertices that have been classified as having a particular
property
("classified" herein). Overlapping dots and/or other indicators can be used to
indicate
multiple classifications (e.g. multiple similar and/or different
classifications). Overlapping
indicators can be displayed in the same location using different radii, height
from the surface
of the anatomy, and/or offsets along the surface of the anatomy in different
directions. In
some embodiments, graphic indicators are displayed "persistently", for example
if a vertex is
classified in a first frame, an indicator of the classification can persist on
the display for one
or more subsequent frames. Additionally or alternatively, an indicator of a
classification can
be displayed for multiple vertices, for example two-edge-connected vertices
for a classified
vertex.
[0261] Referring now to Figs. 9 and 9A, a schematic view of a mapping
catheter, and a
perspective anatomic view of a heart chamber with a mapping catheter inserted
into the
chamber are illustrated, respectively, consistent with the present inventive
concepts. Catheter
10' includes an electrode array 12', comprising one, two, three or more
electrodes 12a. In
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some embodiments, electrode array 12' comprises less than 24 electrodes, such
as less than
12 electrodes, such as 10, 8, 6, 4, or 3 electrodes. Electrode array 12' can
comprise an
expandable array of splines, onto which electrodes 12a are mounted. Catheter
10' can be
percutaneously inserted into a patient, such as to percutaneously deliver
electrode array 12' to
a heart chamber (HC), and can be of similar construction and arrangement as
catheter 10
described hereabove in reference to Fig. 1. Fig. 9A illustrates electrode
array 12'
percutaneously inserted into a heart chamber (RC). Electrodes 12a have been
positioned in
contact with a portion of the heart wall, such that electrical activity data
120a can be
recorded, for example recorded by system 100 as described herein. A region of
analysis is
illustrated, surrounding the tissue proximate the contact locations of
electrodes 12a. In some
embodiments, recorded electrical activity data 120a is processed by system
100, for example
by performing a complexity analysis using algorithm 600 described hereabove in
reference to
Fig. 3, and the diagnostic results 1100 generated can be "assigned" to the
region of analysis
(e.g. the diagnostic results are stored correlating to the vertices of the
anatomic model
represented within the region of analysis). In some embodiments, diagnostic
results 1100
relative to the region of analysis indicate a potential therapeutic benefit
from an intervention
(e.g. an ablation of tissue) at the region of analysis (e.g. with or without
gathering and/or
analyzing data from other areas of the heart chamber). In some embodiments,
several regions
of analysis are interrogated by catheter 10', for example as electrode array
12' is repositioned
against different portions of the heart chamber (HC) and additional data is
recorded and
analyzed.
[0262] The above-described embodiments should be understood to serve only
as
illustrative examples; further embodiments are envisaged. Any feature
described herein in
relation to any one embodiment may be used alone, or in combination with other
features
described, and may also be used in combination with one or more features of
any other of the
embodiments, or any combination of any other of the embodiments. Furthermore,
equivalents and modifications not described above may also be employed without
departing
from the scope of the invention, which is defined in the accompanying claims.
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