U.S. patent application number 17/503657 was filed with the patent office on 2022-05-12 for methods and systems to quantify and remove asynchronous noise in biophysical signals.
The applicant listed for this patent is Analytics For Life Inc.. Invention is credited to Timothy William Fawcett Burton, Abhinav Doomra, Michael Garrett, Shyamlal Ramchandani.
Application Number | 20220142583 17/503657 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-12 |
United States Patent
Application |
20220142583 |
Kind Code |
A1 |
Garrett; Michael ; et
al. |
May 12, 2022 |
METHODS AND SYSTEMS TO QUANTIFY AND REMOVE ASYNCHRONOUS NOISE IN
BIOPHYSICAL SIGNALS
Abstract
The exemplified methods and systems described herein facilitate
the quantification and/or removal of asynchronous noise, such as
muscle artifact noise contamination, to more accurately assess
complex nonlinear variabilities in quasi-periodic
biophysical-signal systems such as those in acquired cardiac
signals, brain signals, etc.
Inventors: |
Garrett; Michael; (Wilmette,
IL) ; Burton; Timothy William Fawcett; (Toronto,
CA) ; Ramchandani; Shyamlal; (Kingston, CA) ;
Doomra; Abhinav; (Toronto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Analytics For Life Inc. |
Toronto |
|
CA |
|
|
Appl. No.: |
17/503657 |
Filed: |
October 18, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16445158 |
Jun 18, 2019 |
11147516 |
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17503657 |
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62686245 |
Jun 18, 2018 |
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International
Class: |
A61B 5/00 20060101
A61B005/00; G06F 16/28 20060101 G06F016/28; G06K 9/62 20060101
G06K009/62; A61B 5/316 20060101 A61B005/316; A61B 5/349 20060101
A61B005/349; A61B 5/369 20060101 A61B005/369 |
Claims
1. A method to filter asynchronous noise from an acquired
biophysical-signal data set, the method comprising: receiving, by a
processor, a biophysical-signal data set of a subject; determining,
by the processor, at least one template-signal vector data set
characteristic of a representative quasi-periodic signal pattern of
the subject from a plurality of detected quasi-periodic cycles
detected in the received biophysical-signal data set; applying, by
the processor, the at least one determined template-signal vector
data set to one or more denoising vector data sets,; and generating
a filtered biophysical-signal data set of the biophysical-signal
data set, or a portion thereof, by merging the portion of the
received biophysical-signal data set to be filtered and the one or
more generated denoising vector data sets.
2-30. (canceled)
Description
RELATED APPLICATIONS
[0001] This application claims to, and the benefit of, U.S.
Provisional Application No. 62/686,245, filed Jun. 18, 2018, titled
"METHODS AND SYSTEMS TO QUANTIFY AND REMOVE ASYNCHRONOUS NOISE IN
BIOPHYSICAL SIGNALS," which is incorporated by reference herein in
its entirety.
FIELD OF THE INVENTION
[0002] The present disclosure generally relates to non-invasive
methods and systems for characterizing cardiovascular circulation
and other physiological systems. More specifically, in an aspect,
the present disclosure relates to the filtering of asynchronous
noise from an acquired biophysical signal (e.g., a cardiac signal,
a brain signal, etc.). In another aspect, the present disclosure
relates to the quality assessment of an acquired signal and the
gating of the acquired signal for analysis. In another aspect, the
present disclosure relates to normalizing a first set of data sets
acquired with a first set of biophysical-signal measurement
equipment and normalizing a second set of data sets acquired with a
second set of biophysical-signal measurement equipment such that
the first set of data sets may be analyzed with the second set of
data sets in a machine learning operation.
BACKGROUND
[0003] Ischemic heart disease, also known as cardiac ischemia or
myocardial ischemia, is a disease or group of diseases
characterized by a reduced blood supply to the heart muscle,
usually due to coronary artery disease (CAD). CAD typically occurs
when the lining inside the coronary arteries that supply blood to
the myocardium, or heart muscle, develops atherosclerosis (the
hardening or stiffening of the lining and the accumulation of
plaque therein, often accompanied by abnormal inflammation). Over
time, CAD can also weaken the heart muscle and contribute to, e.g.,
angina, myocardial infarction (heart attack), heart failure and
arrhythmia. An arrhythmia is an abnormal heart rhythm and can
include any change from the normal sequence of electrical
conduction of the heart and in some cases can lead to cardiac
arrest.
[0004] The evaluation of CAD can be complex, and many techniques
and tools are used to assess the presence and severity of the
condition. In the case of electrocardiography, a field of
cardiology in which the heart's electrical activity is analyzed to
obtain information about its structure and function, significant
ischemic heart disease can alter ventricular conduction properties
of the myocardium in the perfusion bed downstream of a coronary
artery narrowing, or occlusion. This pathology can express itself
at different locations of the heart and at different stages of
severity, making an accurate diagnosis challenging. Further, the
electrical conduction characteristics of the myocardium may vary
from person to person, and other factors such as measurement
variability associated with the placement of measurement probes and
parasitic losses associated with such probes and their related
components can also affect the biophysical signals that are
captured during electrophysiologic tests of the heart. Further
still, when conduction properties of the myocardium are captured as
relatively long cardiac phase gradient signals, they may exhibit
complex nonlinear variability that cannot be efficiently captured
by traditional modeling techniques.
[0005] The quantification and filtering of asynchronous noise and
artifacts in acquired biophysical signals, e.g., cardiac signals,
brain signals, etc., that can facilitate more accurate assessments
of pathologies and conditions is desired.
SUMMARY
[0006] The exemplified methods and systems described herein
facilitate the quantification and/or removal of asynchronous noise,
such as skeletal-muscle artifact noise contamination, to more
accurately assess complex nonlinear variabilities in quasi-periodic
biophysical-signal systems such as those in acquired cardiac
signals, brain signals, etc. The exemplified methods and systems
described herein further facilitate the assessment of
signal-quality of an acquired signal for gating the acquired signal
for subsequent analysis.
[0007] The term "cardiac signals" (also referred to as heart
signals), as used herein, refers to signals associated with the
function and/or activity of the electrical conduction system of the
heart, e.g., to cause contraction of the myocardium, and includes,
in some embodiments, electrocardiographic signals such as those
acquired via an electrocardiogram (ECG). The quantification of
levels of asynchronous noise such as skeletal-muscle-related-signal
contamination and muscle-artifact-noise contamination, and other
asynchronous-noise contamination in an acquired signal can be
subsequently used for the automated rejection of such asynchronous
noise from measurements of biophysical signals, such as cardiac
signals, to which the presence of such asynchronous noise could
have a negative impact to subsequent analyses of the cardiac
signals and/or biophysical signals and/or to the clinical
prediction/estimation of disease state that assess for various
quasi-periodic features of such quasi-periodic biophysical
signal.
[0008] The term "brain signals" (also referred to herein as
neurological signals), as used herein, refers to signals associated
with the brain functions/activities and include, in some
embodiments, electroencephalographic signals such as those acquired
via an electroencephalogram (EEG). The quantification of levels of
asynchronous noise such as extraocular-muscle noise contamination
and facial muscle noise contamination, and other asynchronous noise
contamination in an acquired signal can be subsequently used for
the automated or manual rejection of such asynchronous noise from
measurements of biophysical signals, such as brain signals, to
which the presence of such asynchronous noise could have a negative
impact to subsequent analyses of the brain signals and/or
biophysical signals and/or to the clinical prediction/estimation of
disease state(s) that assess for various quasi-periodic features of
such.
[0009] For purposes of the present disclosure, the term
"biophysical signal" is not meant to be limited to cardiac signals
and brain signals, but encompasses any mammalian electrical or
electrochemical signal capable of being sensed, including without
limitation those associated with the central and peripheral nervous
systems (e.g., electrical signals from the brain, spinal cord,
and/or nerves and their associated neurons), pulmonary, circulatory
(e.g., blood), lymphatic, endocrine, digestive, musculoskeletal,
urinary, immune, reproductive, integumentary and reproductive
systems, as well as electrical signals generated at the cellular
level in any place in a mammalian body. While the present
disclosure is directed to the beneficial quantification of
asynchronous noise in the diagnosis and treatment of
cardiac-related pathologies and conditions and/or brain-related
pathologies and conditions (including, e.g., coronary arterial
disease and pulmonary hypertension (e.g., pulmonary arterial
hypertension, pulmonary hypertension due to left heart disease,
pulmonary hypertension due to lung disease, pulmonary hypertension
due to chronic blood clots, and pulmonary hypertension due to other
disease), as well as other cardiac-related conditions and/or
disease and/or brain-related conditions and/or disease mentioned
herein), such quantification can be applied to the diagnosis and
treatment (including pharmacologic treatment) of any pathologies or
conditions in which a biophysical signal is involved in any
relevant system of the mammalian body.
[0010] Skeletal-muscle-related signals (e.g., as characterized in
electromyograms (EMG)) are often characterized as being "in-band
noise" with respect to a cardiac signal, a brain signal, etc.--that
is, it often occurs in the same or similar frequency range within
the acquired biophysical signal. For example, for cardiac signals,
the dominant frequency components of signals produced are often
between about 0.5 Hz and about 80 Hz. For brain signals, the
frequency components are often between about 0.1 Hz and about 50
Hz. Also, depending on the degree of contamination,
skeletal-muscle-related signals can also have a same, or similar,
amplitude as typical cardiac-based waveforms and brain-based
waveforms, etc. Indeed, similarity of skeletal-muscle-related
signals to cardiac signals, brain signals, etc., can cause
significant issues for the automated diagnostic analysis of
biophysical signals. Therefore, quantifying the level of
skeletal-muscle-related contamination and other asynchronous noise
in a measured biophysical signal can be critical for either the
quality assessment of acquired biophysical signals and the
automated rejection of contaminated acquired signals from being
used in subsequent analyses, and/or providing information to the
subsequent analyses to enable compensation for the
contamination.
[0011] A critical observation when quantifying the level of
skeletal-muscle-related signal in an acquired biophysical signal,
such as cardiac signal, brain signal, etc., is that skeletal-muscle
related signals are not in synchrony with the cardiac signal, brain
signal, etc., because the sources of the skeletal-muscle-related
signal and the biophysical signals are completely different. For
example, cardiac signals are derived from the summation of the
action potentials of the cardiac myocytes brain signals are derived
from the summation of ionic current within the neurons of the
brain, while the skeletal-muscle related signals are derived from
the summation of the action potentials of an originating muscle
(such as the pectoral muscles, biceps, triceps, etc. Those two
sources are unlikely to share a deeper common source that could
create synchronicity.
[0012] Therefore, skeletal-muscle related signals (and other
asynchronous artifacts) can be quantified by comparing, as
described herein, acquired biophysical signal(s) and cycles therein
to an idealized, representative biophysical signal for that patient
to which gross differences between the acquired and idealized
biophysical signals can be accounted for as contribution of
skeletal-muscle related signals contamination (and other
asynchronous signals) in the acquired biophysical signal.
[0013] In an aspect, a method is disclosed to filter asynchronous
noise (skeletal-muscle artifact noise and other asynchronous noise)
from an acquired biophysical-signal data set. The method includes
receiving, by a processor, a biophysical-signal data set of a
subject; determining, by the processor, at least one
template-signal vector data set characteristic of a representative
quasi-periodic signal pattern (e.g., a representative heart-beat
pattern) of the subject from a plurality of detected quasi-periodic
signal cycles detected in the received biophysical-signal data set;
applying, by the processor, the at least one determined
template-signal vector data set to one or more denoising vector
data sets, wherein the one or more denoising vector data sets
collectively have a vector length corresponding to a vector length
of a portion of the received biophysical-signal data set to be
filtered, and wherein the at least one determined template-signal
vector data set is i) applied for each of the detected cycles
determined to be present in the portion of received cardiac signal
data set to be filtered and ii) varied in length to match the
vector length of a corresponding detected cycle of the portion of
the received biophysical-signal data set to be filtered; and
generating a filtered biophysical-signal data set of the
biophysical-signal data set, or a portion thereof, by merging the
portion of the received biophysical-signal data set to be filtered
and the one or more generated denoising vector data sets (e.g.,
using a window-based operation that applies, in the frequency
domain, weighted averages of the received biophysical-signal and
the one or more generated denoising vectors).
[0014] In some embodiments, the method further includes receiving,
by the processor, one or more additional biophysical-signal data
sets each contemporaneously acquired from the subject with the
biophysical-signal data set; determining, by the processor, at
least one template-signal vector data set characteristic of a
representative quasi-periodic signal pattern of the subject from a
plurality of detected heart-beat cycles detected in each of the
received one or more additional biophysical-signal data sets;
applying, by the processor, for each of the received one or more
additional biophysical-signal data sets, a plurality of determined
template-signal vector data sets to one or more denoising vector
data sets in a repeating manner, wherein the one or more denoising
vector data sets collectively have a vector length corresponding to
a vector length of a portion of the received additional
biophysical-signal data sets to be filtered, and wherein each of
the plurality of determined template-signal vector data sets is i)
applied for each of the detected cycles determined to be present in
the portion of received additional biophysical-signal data sets to
be filtered and ii) varied in length to match the vector length of
a corresponding detected cycle of the portion of the received
additional biophysical-signal data sets to be filtered; and
generating a filtered biophysical-signal data set of the
biophysical-signal data set, or a portion thereof, by merging the
portion of the received biophysical-signal data set to be filtered
and the one or more generated denoising vector data sets (e.g.,
using a window-based operation that applies, in the frequency
domain, weighted averages of the received biophysical-signal data
set and the one or more generated denoising vector data sets).
[0015] In some embodiments, the step of determining the at least
one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern comprises:
determining, by the processor, a plurality of signal features
(e.g., R-peaks for cardiac signals) characteristically distinct in
the received biophysical-signal data set or a portion thereof;
determining, by the processor, a plurality of cycle regions (e.g.,
a median R-R interval) (e.g., stored in a M.times.N matrix in which
M is a number of detected cycles, and N is about 40% of the median
R-R interval) between each of the plurality of determined signal
features; aligning, by the processor, each of the plurality of
cycle regions to each other to a same aspect of the plurality of
signal features or another set of signal features located in each
of the cycle regions (e.g., for cardiac signals, features can
include initiation of the Q wave, or the peak of the R wave, or
delay estimate by cross correlation); and determining, by the
processor, each point of the at least one template-signal vector
data set using a mean operation or a median operation performed for
each set of points among the plurality of cycle regions.
[0016] In some embodiments, the received biophysical-signal data
set comprises a cardiac signal data set, and wherein the plurality
of signal features are selected from the group consisting of:
R-peaks in the received cardiac signal data set or a portion
thereof, S-peaks in the received cardiac signal data set or a
portion thereof, T-peaks in the received cardiac signal data set or
a portion thereof, Q-peaks in the received cardiac signal data set
or a portion thereof, and P-peaks in the received cardiac signal
data set or a portion thereof.
[0017] In some embodiments, the received biophysical-signal data
set comprises a cardiac signal data set, wherein the plurality of
signal features correspond to R-peaks in the received cardiac
signal data set or a portion thereof.
[0018] In some embodiments, the step of determining the at least
one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern further comprises
determining, by the processor, a normalizing parameter (e.g.,
z-score) derived from each the plurality of cycle regions.
[0019] In some embodiments, the step of determining the at least
one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern further comprises
normalizing, by the processor, values, or a parameter derived
therefrom (e.g., z-score), of each of the plurality of cycle
regions to a pre-defined scale (e.g., between "0" and "1" or
between "-1" and "1", or between a standard deviation value greater
than 0 and less than 10, etc.).
[0020] In some embodiments, the step of determining the at least
one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern further comprises
performing, by the processor, clustering-based analysis (e.g.,
PCA+DBSCAN) of the plurality of cycle regions to determine presence
of more than one dominant cycle morphologies, wherein a
template-signal vector is determined for each determined dominant
cycle morphology.
[0021] In some embodiments, the plurality of cycle regions
comprises cycles that are neighboring one another.
[0022] In some embodiments, the cycles that are neighboring one
another overlaps in part to one another.
[0023] In some embodiments, the cycles that are neighboring one
another do not overlap to one another.
[0024] In some embodiments, the filtered biophysical signal data
set is generated by using two or more template-signal vector data
sets from two or more group of cycles of the plurality of cycle
regions, wherein the two or more groups of cycles of the plurality
of cycle regions are neighboring one another.
[0025] In some embodiments, the filtered biophysical-signal data
set is generated in near real-time as the biophysical-signal (e.g.,
cardiac signal, pulmonary signal, brain signal) is acquired.
[0026] In some embodiments, the filtered biophysical-signal data
set is generated following completed acquisition of the biophysical
signal.
[0027] In some embodiments, the one or more denoising vector data
sets are arranged as a 1-dimensional vector.
[0028] In some embodiments, the one or more denoising vector data
sets are arranged as an N-dimensional vector, wherein N corresponds
to a number of detected cycles determined to be present in the
portion of received biophysical-signal data set to be filtered.
[0029] In some embodiments, the step of applying the plurality of
the determined template-signal vector data sets to one or more
denoising vector data sets comprises initializing, by the
processor, the one or more denoising vector data set as a
1-dimensional vector having a length corresponding to that of the
portion of received biophysical signal to be filtered; and
duplicating, by the processor, the determined template-signal
vectors in the 1-dimensional vector so as to align at least a data
point associated with a peak (e.g., R-peak or cardiac signals) of
the determined template-signal vectors to each peak (e.g., R-peak)
determined in the received biophysical signal to be filtered.
[0030] In some embodiments, during the duplication step, conflict
portions of a currently duplicating template-signal vector data set
are assigned average values with respect to corresponding portions
of a previously duplicated template-signal vector data set to which
the currently duplicating template-signal vector data set has a
conflict.
[0031] In some embodiments, during the duplication step, empty
regions in the 1-dimensional vector between a currently duplicating
template-signal vector data set and a previously duplicated
template-signal vector data set are stored with values interpolated
between a last filled value and a next filled value around the
empty region.
[0032] In some embodiments, the window-based operation comprises:
scaling, by the processor, the portion of the received
biophysical-signal data set to be filtered with a plurality of
window functions having a pre-defined window length to generate a
modified biophysical-signal data set; scaling, by the processor,
the one or more generated denoising vector data sets with the
plurality of window functions to generate a modified denoising
vector data sets; determining, by the processor, an envelope of the
modified denoising vector data sets (e.g., by using a low-pass
filter); converting, by the processor, via a FFT operation, the
envelope of the modified denoising vector data sets and of the
portion of the received biophysical-signal data set to be filtered
to the frequency domain; performing, by the processor, a weighted
average operation of the FFT envelope of the modified denoising
vector data sets and of the modified biophysical-signal data set
using a static, or a set of dynamic, interpolation coefficients to
generate a resulting data set; and converting, by the processor,
via an inverse FFT operation, the resulting data set to a time
series data set as the filtered biophysical-signal data set of the
biophysical signal.
[0033] In another aspect, a method is disclosed of normalizing a
first set of data sets acquired with a set of first measurement
equipment (e.g., by removing asynchronous noise) and a second set
of data sets acquired with a second set of measurement equipment
(e.g., that is configured to remove certain asynchronous noise)
such that the first set of data sets is analyzable with the second
set of data sets in a machine learning operation. The method
includes receiving, by a processor, a set of biophysical-signal
data sets of a subject acquired with a set of first measurement
equipment (e.g., each equipment of the set of first measurement
equipment has a similar or same noise performance characteristic);
determining, by the processor, at least one template-signal vector
data set characteristic of a representative quasi-periodic signal
pattern of the subject from a plurality of detected quasi-periodic
signal cycles detected in the received biophysical-signal data set;
applying, by the processor, a plurality of the determined
template-signal vector data sets, or a vector selected from the
group thereof, to one or more denoising vector data sets, wherein
the one or more denoising vector data sets collectively have a
vector length corresponding to a vector length of a portion of the
received biophysical-signal data set to be filtered, wherein each
applied template-signal vector data set is i) applied for each of
the detected cycles determined to be present in the portion of
received biophysical-signal data set to be filtered and ii) varied
in length to match the vector length of a corresponding detected
cycle of the portion of the received biophysical-signal data set to
be filtered; and generating a filtered biophysical-signal data set
associated with the biophysical-signal data set, or a portion
thereof, as a normalized data set of the biophysical signal,
wherein the filtered biophysical signal is generated by merging the
portion of the received biophysical signal to be filtered and the
one or more generated denoising vectors (e.g., using a window-based
operation that applies, in the frequency domain, weighted averages
of the received biophysical signal and the one or more generated
denoising vectors), wherein the normalized data set associated with
the biophysical signal acquired with the first measurement
equipment is analyzable (e.g., with skeletal-muscle-related
noise/muscle artifact noise removed) as a machine-learning training
data set along with a second data set acquired with a second
measured equipment.
[0034] In some embodiments, a data set of the received biophysical
signal comprises data captured from sensors (e.g., in a smart
device or in a handheld medical diagnostic equipment) selected from
the group consisting of a 12-lead surface potential sensing
electrode system (e.g., electrocardiogram system), an intracardiac
electrocardiogram, a Holter electrocardiogram, a 6-lead
differential surface potential sensing electrode system, a 3-lead
orthogonal surface potential sensing electrode system, and a single
lead potential sensing electrode system.
[0035] In some embodiments, a data set of the received biophysical
signal comprises wide-band cardiac phase gradient cardiac signal
data (e.g., having at a sampling frequency above about 1 KHz, e.g.,
above about 10 KHz, above about 40 KHz, above about 80 KHz, above
about 500 KHz) derived from biopotential signals simultaneously
captured (e.g., having a skew less than about 100 microseconds)
from a plurality of surface electrode placed on surfaces of a body
in proximity to a heart.
[0036] In another aspect, a method is disclosed of rejecting an
acquired signal, the method comprising: receiving, by a processor,
a biophysical-signal data set of a subject; comparing, by the
processor, the received biophysical-signal data set to at least one
template-signal vector data set characteristic of a representative
quasi-periodic signal pattern within the biophysical-signal data
set; and rejecting, by the processor, the received
biophysical-signal data set based on the comparison.
[0037] In some embodiments, the step of determining the at least
one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern comprises:
determining, by the processor, a plurality of signal features
(e.g., R-peaks for cardiac signals) characteristically distinct in
the received biophysical-signal data set or a portion thereof;
determining, by the processor, a plurality of cycle regions (e.g.,
a median R-R interval) (e.g., stored in a M.times.N matrix in which
M is a number of detected cycles, and N is about 40% of the median
R-R interval) between each of the plurality of determined signal
features; aligning, by the processor, each of the plurality of
cycle regions to each other to a same aspect of the plurality of
signal features or another set of signal features located in each
of the cycle regions (e.g., for cardiac signals, features can
include initiation of the Q wave, or the peak of the R wave, or
delay estimate by cross correlation); and determining, by the
processor, each point of the at least one template-signal vector
data set using a mean operation or a median operation performed for
each set of points among the plurality of cycle regions.
[0038] In some embodiments, the step of determining the at least
one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern further comprises:
performing, by the processor, clustering-based analysis (e.g.,
PCA+DBSCAN) of the plurality of cycle regions to determine presence
of more than one dominant cycle morphologies, wherein a
template-signal vector is determined for each determined dominant
cycle morphology.
[0039] In some embodiments, the method further includes generating,
by the processor, a notification of a failed acquisition of
biophysical-signal data set, wherein the notification prompts a
subsequent acquisition of the biophysical-signal data set to be
performed.
[0040] In some embodiments, the method further includes causing, by
the processor, transmission of the received biophysical-signal data
set over a network to an external analysis system, wherein the
analysis system is configured to analyze the received
biophysical-signal data for presence, or degree, of a pathology or
clinical condition.
[0041] In some embodiments, the comparison comprises determining
presence of asynchronous noise present in the acquired
biophysical-signal data set having a value or energy over a
pre-defined threshold.
[0042] In another aspect, a method is disclosed of quantifying
asynchronous noise in an acquired biophysical signal. The method
includes receiving, by a processor, a biophysical-signal data set
of a subject; determining, by the processor, a plurality of signal
features (e.g., R-peaks for cardiac signals) characteristically
distinct in the received biophysical-signal data set or a portion
thereof; determining, by the processor, a plurality of cycle
regions (e.g., a median R-R interval) (e.g., stored in a M.times.N
matrix in which M is a number of detected cycles, and N is about
40% of the median R-R interval) between each of the plurality of
determined signal features; aligning, by the processor, each of the
plurality of cycle regions to each other to a same aspect of the
plurality of signal features or another set of signal features
located in each of the cycle regions (e.g., for cardiac signals,
features can include initiation of the Q wave, or the peak of the R
wave, or delay estimate by cross correlation); determining, by the
processor, each point of the at least one template-signal vector
data set using a mean operation or a median operation performed for
each set of points among the plurality of cycle regions; and
performing, by the processor, clustering-based analysis (e.g.,
PCA+DBSCAN) of the plurality of cycle regions to determine presence
of more than one dominant cycle morphologies, wherein a
template-signal vector is determined for each determined dominant
cycle morphology.
[0043] In an aspect, a system is disclosed to filter asynchronous
noise from an acquired biophysical-signal data set, the system
comprising: a processor and a memory having instructions stored
thereon, wherein execution of the instructions by the processor
cause the processor to receive a biophysical-signal data set of a
subject; determine at least one template-signal vector data set
characteristic of a representative quasi-periodic signal pattern of
the subject from a plurality of detected quasi-periodic cycles
detected in the received biophysical-signal data set; apply the at
least one determined template-signal vector data set to one or more
denoising vector data sets, wherein the one or more denoising
vector data sets collectively have a vector length corresponding to
a vector length of a portion of the received biophysical-signal
data set to be filtered, and wherein the at least one determined
template-signal vector data set is i) applied for each of the
detected cycles determined to be present in the portion of received
cardiac signal data set to be filtered and ii) varied in length to
match the vector length of a corresponding detected cycle of the
portion of the received biophysical-signal data set to be filtered;
and generate a filtered biophysical-signal data set of the
biophysical-signal data set, or a portion thereof, by merging the
portion of the received biophysical-signal data set to be filtered
and the one or more generated denoising vector data sets.
[0044] In some embodiments, the instructions when executed by the
processor further cause the processor to receive one or more
additional biophysical signal data sets each contemporaneously
acquired from the subject with the biophysical signal data set;
determine at least one template-signal vector data set
characteristic of a representative quasi-periodic signal pattern of
the subject from a plurality of detected heart-beat cycles detected
in each of the received one or more additional biophysical signal
data sets; apply for each of the received one or more additional
biophysical signal data sets, a plurality of determined
template-signal vector data sets to one or more denoising vector
data sets in a repeating manner, wherein the one or more denoising
vector data sets collectively have a vector length corresponding to
a vector length of a portion of the received additional biophysical
signal data sets to be filtered, and wherein each of the plurality
of determined template-signal vector data sets is i) applied for
each of the detected cycles determined to be present in the portion
of received additional biophysical signal data sets to be filtered
and ii) varied in length to match the vector length of a
corresponding detected cycle of the portion of the received
additional biophysical signal data sets to be filtered; and
generate a filtered biophysical signal data set of the biophysical
signal data set, or a portion thereof, by merging the portion of
the received biophysical signal data set to be filtered and the one
or more generated denoising vector data sets.
[0045] In some embodiments, the operation of determining the at
least one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern comprises determining,
by the processor, a plurality of signal features characteristically
distinct in the received biophysical signal data set or a portion
thereof; determining, by the processor, a plurality of cycle
regions between each of the plurality of determined signal
features;
[0046] aligning, by the processor, each of the plurality of cycle
regions to each other to a same aspect of the plurality of signal
features or another set of signal features located in each of the
cycle regions; and determining, by the processor, each point of the
at least one template-signal vector data set using a mean operation
or a median operation performed for each set of points among the
plurality of cycle regions.
[0047] In some embodiments, the received biophysical-signal data
set comprises a cardiac signal data set, and wherein the plurality
of signal features are selected from the group consisting of:
R-peaks in the received cardiac signal data set or a portion
thereof, S-peaks in the received cardiac signal data set or a
portion thereof, T-peaks in the received cardiac signal data set or
a portion thereof, Q-peaks in the received cardiac signal data set
or a portion thereof, and P-peaks in the received cardiac signal
data set or a portion thereof.
[0048] In some embodiments, the received biophysical-signal data
set comprises a cardiac signal data set, and wherein the plurality
of signal features correspond to R-peaks in the received cardiac
signal data set or a portion thereof.
[0049] In some embodiments, the operation of determining the at
least one template-signal vector data set characteristic of the
representative quasi-periodic pattern further comprises
determining, by the processor, a normalizing parameter derived from
each the plurality of cycle regions.
[0050] In some embodiments, the operation of determining the at
least one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern further comprises
normalizing, by the processor, values, or a parameter derived
therefrom, of each of the plurality of cycle regions to a
pre-defined scale.
[0051] In some embodiments, the operation of determining the at
least one template-signal vector data set characteristic of the
representative quasi-periodic signal pattern further comprises
performing, by the processor, clustering-based analysis of the
plurality of cycle regions to determine presence of more than one
dominant cycle morphologies, wherein a template-signal vector is
determined for each determined dominant cycle morphology.
[0052] In some embodiments, the plurality of cycle regions
comprises cycles that are neighboring one another.
[0053] In some embodiments, the cycles that are neighboring one
another overlaps in part to one another.
[0054] In some embodiments, the cycles that are neighboring one
another do not overlap to one another.
[0055] In some embodiments, the filtered biophysical signal data
set is generated by using two or more template-signal vector data
sets from two or more group of cycles of the plurality of cycle
regions, wherein the two or more groups of cycles of the plurality
of cycle regions are neighboring one another.
[0056] In some embodiments, the filtered biophysical signal data
set is generated in near real-time as the biophysical signal is
acquired.
[0057] In some embodiments, the filtered biophysical-signal data
set is generated following completed acquisition of the biophysical
signal.
[0058] In some embodiments, the one or more denoising vector data
sets are arranged as a 1-dimensional vector.
[0059] In some embodiments, the one or more denoising vector data
sets are arranged as an N-dimensional vector, wherein N corresponds
to a number of detected cycles determined to be present in the
portion of received biophysical signal data set to be filtered.
[0060] In some embodiments, the operation applying the plurality of
the determined template-signal vector data sets to one or more
denoising vector data sets comprises initializing, by the
processor, the one or more denoising vector data set as a
1-dimensional vector having a length corresponding to that of the
portion of received biophysical signal to be filtered; and
duplicating, by the processor, the determined template-signal
vectors in the 1-dimensional vector so as to align at least a data
point associated with a peak of the determined template-signal
vectors to each peak determined in the received biophysical signal
to be filtered.
[0061] In some embodiments, conflict portions of a currently
duplicating template-signal vector data set are assigned, during
the duplication step, average values with respect to corresponding
portions of a previously duplicated template-signal vector data set
to which the currently duplicating template-signal vector data set
has a conflict.
[0062] In some embodiments, empty regions in the 1-dimensional
vector between a currently duplicating template-signal vector data
set and a previously duplicated template-signal vector data set are
stored, during the duplication step, with values interpolated
between a last filled value and a next filled value around the
empty region.
[0063] In some embodiments, the window-based operation comprises
scaling, by the processor, the portion of the received biophysical
signal data set to be filtered with a plurality of window functions
having a pre-defined window length to generate a modified
biophysical signal data set; scaling, by the processor, the one or
more generated denoising vector data sets with the plurality of
window functions to generate a modified denoising vector data sets;
determining, by the processor, an envelope of the modified
denoising vector data sets; converting, by the processor, via a FFT
operation, the envelope of the modified denoising vector data sets
and of the portion of the received biophysical signal data set to
be filtered to the frequency domain; performing, by the processor,
a weighted average operation of the FFT envelope of the modified
denoising vector data sets and of the modified biophysical signal
data set using a static, or a set of dynamic, interpolation
coefficients to generate a resulting data set; and converting, by
the processor, via an inverse FFT operation, the resulting data set
to a time series data set as the filtered biophysical signal data
set of the biophysical signal.
[0064] In another aspect, a system is disclosed of normalizing a
first set of data sets acquired with a set of first measurement
equipment and a second set of data sets acquired with a second set
of measurement equipment such that the first set of data sets is
analyzable with the second set of data sets in a machine learning
operation, the system includes a processor and a memory having
instructions stored thereon, wherein execution of the instructions
by the processor cause the processor to receive a set of
biophysical-signal data sets of a subject acquired with a set of
first measurement equipment; determine at least one template-signal
vector data set characteristic of a representative quasi-periodic
signal pattern of the subject from a plurality of detected
quasi-periodic signal cycles detected in the received
biophysical-signal data set; apply a plurality of the determined
template-signal vector data sets, or a vector selected from the
group thereof, to one or more denoising vector data sets, wherein
the one or more denoising vector data sets collectively have a
vector length corresponding to a vector length of a portion of the
received biophysical signal data set to be filtered, wherein each
applied template-signal vector data set is i) applied for each of
the detected cycles determined to be present in the portion of
received biophysical signal data set to be filtered and ii) varied
in length to match the vector length of a corresponding detected
cycle of the portion of the received biophysical signal data set to
be filtered; and generate a filtered biophysical signal data set
associated with the biophysical signal data set, or a portion
thereof, as a normalized data set of the biophysical signal,
wherein the filtered biophysical signal is generated by merging the
portion of the received biophysical signal to be filtered and the
one or more generated denoising vectors, wherein the normalized
data set associated with the biophysical signal acquired with the
first measurement equipment is analyzable as a machine-learning
training data set along with a second data set acquired with a
second measured equipment.
[0065] In some embodiments, the system comprises a sensor device
selected from the group consisting of a 12-lead surface potential
sensing electrode system, an intracardiac electrocardiogram, a
Holter electrocardiogram, a 6-lead differential surface potential
sensing electrode system, a 3-lead orthogonal surface potential
sensing electrode system, and a single lead potential sensing
electrode system.
[0066] In some embodiments, the system comprises a sensor device
configured to acquire biopotential signals simultaneously captured
from a plurality of surface electrode placed on surfaces of a body
in proximity to a heart.
[0067] In another aspect, a system is disclosed of rejecting an
acquired biophysical signal, the system comprising a processor and
a memory having instructions stored thereon, wherein execution of
the instructions by the processor cause the processor to receive a
biophysical-signal data set of a subject; compare the received
biophysical-signal data set to at least one template-signal vector
data set characteristic of a representative quasi-periodic pattern
within the biophysical-signal data set; and reject the received
biophysical-signal data set based on the comparison.
[0068] In some embodiments, the operation of determining the at
least one template-signal vector data set characteristic of the
representative quasi-periodic pattern comprises determining, by the
processor, a plurality of signal features characteristically
distinct in the received biophysical-signal data set or a portion
thereof; determining, by the processor, a plurality of cycle
regions between each of the plurality of determined signal
features; aligning, by the processor, each of the plurality of
cycle regions to each other to a same aspect of the plurality of
signal features or another set of signal features located in each
of the cycle regions; and determining, by the processor, each point
of the at least one template-signal vector data set using a mean
operation or a median operation performed for each set of points
among the plurality of cycle regions.
[0069] In some embodiments, the operation of determining the at
least one template-signal vector data set characteristic of the
representative quasi-periodic pattern further comprises performing,
by the processor, clustering-based analysis of the plurality of
cycle regions to determine presence of more than one dominant cycle
morphologies, wherein a template-signal vector is determined for
each determined dominant cycle morphology.
[0070] In some embodiments, the instructions when executed by the
processor further cause the processor to generate a notification of
a failed acquisition of biophysical-signal data set, wherein the
notification prompts a subsequent acquisition of the
biophysical-signal data set to be performed.
[0071] In some embodiments, the instructions when executed by the
processor further cause the processor to cause transmission of the
received biophysical-signal data set over a network to an external
analysis system, wherein the analysis system is configured to
analyze the received biophysical-signal data for presence, or
degree, of a pathology or clinical condition.
[0072] In some embodiments, the comparison comprises the operation
of determining presence of asynchronous noise present in the
acquired biophysical-signal data set having a value or energy over
a pre-defined threshold.
[0073] In another aspect, a system is disclosed that is configured
to quantify asynchronous noise in an acquired biophysical signal.
The system includes a processor and a memory having instructions
stored thereon, wherein execution of the instructions by the
processor cause the processor to receive a biophysical-signal data
set of a subject; determine a plurality of signal features
characteristically distinct in the received biophysical-signal data
set or a portion thereof; determine a plurality of cycle regions
between each of the plurality of determined signal features; align
each of the plurality of cycle regions to each other to a same
aspect of the plurality of signal features or another set of signal
features located in each of the cycle regions; determine each point
of the at least one template-signal vector data set using a mean
operation or a median operation performed for each set of points
among the plurality of cycle regions; and perform clustering-based
analysis of the plurality of cycle regions to determine presence of
more than one dominant cycle morphologies, wherein a
template-signal vector is determined for each determined dominant
cycle morphology.
[0074] In another aspect, a system is disclosed comprising: one or
more processors; and a memory having instructions stored thereon,
wherein execution of the instruction by the one or more processor,
cause the one or more processors to perform any one of the
above-recited method.
[0075] In another aspect, a non-transitory computer readable medium
is disclosed, the computer readable medium having instructions
stored thereon, wherein execution of the instruction by one or more
processors, cause the one or more processors to perform any one of
the above-recited method.
BRIEF DESCRIPTION OF THE DRAWINGS
[0076] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate embodiments and
together with the description, serve to explain the principles of
the methods and systems contained herein. The patent or application
file contains at least one drawing executed in color. Copies of
this patent or patent application publication with color drawing(s)
will be provided by the Office upon request and payment of the
necessary fee.
[0077] Embodiments of the present invention may be better
understood from the following detailed description when read in
conjunction with the accompanying drawings. The drawings include
the following figures:
[0078] FIG. 1A is a diagram of an example system configured to
quantify and remove asynchronous noise and artifact contamination
to more accurately assess complex nonlinear variabilities in
quasi-periodic systems, in accordance with an illustrative
embodiment.
[0079] FIG. 1B is a diagram of an example system configured to
reject an acquired biophysical signal based on a quantification of
asynchronous noise and artifact contamination, in accordance with
another illustrative embodiment.
[0080] FIG. 1C is a diagram of an example system configured to
remove asynchronous noise and/or reject an acquired biophysical
signal, in accordance with another illustrative embodiment.
[0081] FIG. 2 shows an exemplary method of removing asynchronous
noise from an acquired biophysical signal (e.g., acquired cardiac
signal, acquired brain signal, etc.) in accordance with an
illustrative embodiment.
[0082] FIG. 3 is a diagram showing an example implementation method
of the process of FIG. 2, in accordance with an illustrative
embodiment.
[0083] FIG. 4 is a flow diagram of an example method of
representative cycle data set in accordance with an illustrative
embodiment.
[0084] FIG. 5 is a diagram of an example method to quantify
asynchronous noise contamination in a biophysical signal, in
accordance with an illustrative embodiment.
[0085] FIG. 6 is a diagram of a representative cycle data set
characteristic of a representative quasi-periodic signal pattern
(e.g., representative heart-beat pattern in an acquired cardiac
signal), in accordance with an illustrative embodiment.
[0086] FIG. 7 shows a diagram of a method to generate the
template-vector data set (e.g., in an acquired cardiac signal), in
accordance with an illustrative embodiment.
[0087] FIG. 8 is an example plot of the raw biophysical-signal data
set, a generated biophysical template-vector data set, and a
resulting denoised biophysical-signal data set, in accordance with
an illustrative embodiment.
[0088] FIG. 9 shows a diagram of a process to segment biophysical
cycles (e.g., cardiac cycles) from the biophysical-signal data set
(e.g., cardiac signal data set) to quantify asynchronous noise
contamination in the biophysical-signal data set, in accordance
with an illustrative embodiment.
[0089] FIG. 10A shows a plot of results of the normalization
process of FIG. 4 in accordance with an illustrative
embodiment.
[0090] FIG. 10B shows a template-signal vector data set
superimposed on top of a set of stacked cycles for a high-noise
signal.
[0091] FIG. 10C shows a template-signal vector data set
superimposed on top of a set of stacked cycles for a low-noise
signal.
[0092] FIG. 11 shows an example output of a principal component
analysis of a generated cycle matrix, in accordance with an
illustrative embodiment.
[0093] FIG. 12 is a plot of a distribution of difference score
determined based on a comparison of the representative cycle data
set and each evaluated cycle, in accordance with an illustrative
embodiment.
[0094] FIGS. 13A, 13B, and 13C show an example wide-band cerebral
phase gradient signal data set acquired from the measurement system
of FIG. 1A, in accordance with an illustrative embodiment.
[0095] FIG. 14 illustrates the wide-band cerebral phase gradient
signals of FIGS. 13A-13C presented in phase space, in accordance
with an illustrative embodiment.
[0096] FIG. 15 is a diagram of a method to normalize a first set of
data sets acquired with a first set of biophysical-signal
measurement equipment and a second set of data sets acquired with a
second set of biophysical-signal measured equipment such that the
first set of data sets may be analyzed with the second set of data
sets in a machine learning operation, in accordance with an
illustrative embodiment.
[0097] FIG. 16 is an example method of analysis by the non-invasive
cardiac assessment system in accordance with an implementation of
the present disclosure.
DETAILED SPECIFICATION
[0098] Each and every feature described herein, and each and every
combination of two or more of such features, is included within the
scope of the present invention provided that the features included
in such a combination are not mutually inconsistent.
[0099] Example System
[0100] FIG. 1A is a diagram of an example system 100 configured to
quantify and remove asynchronous noise such as
skeletal-muscle-related artifact noise contamination and using such
quantification to more accurately assess complex nonlinear
variabilities in quasi-periodic systems, in accordance with an
illustrative embodiment. As used herein, the term "remove" refers
to any meaningful reduction, in whole or in part, in noise
contamination that improves or benefits subsequent analysis.
[0101] In FIG. 1A, a non-invasive measurement system 102 acquires a
plurality of biophysical signals 104 (e.g., phase gradient
biopotential signals) via any number of measurement probes 114
(shown in the system 100 of FIG. 1 as including six such probes
114a, 114b, 114c, 114d, 114e, and 114f) from a subject 106 to
produce a phase-gradient biophysical-signal data set 108 that is
made available to a non-invasive biophysical-signal assessment
system 110 (labeled in FIG. 1 as a "non-invasive biophysical-signal
assessment system" 110) to determine a clinical output 112. In some
embodiments, the clinical output includes an assessment of presence
or non-presence of a disease and/or an estimated physiological
characteristic of the physiological system under study.
[0102] In some embodiments, and as shown in FIG. 1A, the system 102
is configured to remove asynchronous noise contamination (e.g., via
process 118) from the acquired biophysical-signal data set 117
generated by a front-end amplification and digitization operation
116. The removal operation 118 is based on a quantification of the
asynchronous noise potentially present in the acquired signal 114.
The process 118 of removing asynchronous noise could be performed
in near real-time once a representative cycle data set is
established, e.g., from a few samples of the acquired data set. In
some embodiments, a few hundred samples can be used to establish
representative cycle data set. In other embodiments, a few thousand
samples can be used to establish a representative cycle data
set.
[0103] The measurement system 102, in some embodiments, includes a
biopotential-based measurement system configured to acquire
wide-band biopotential biophysical signals. In the
electrocardiography context, the measurement system 102 is
configured to capture cardiac-related biopotential or
electrophysiological signals of a mammalian subject (such as a
human) as wide-band cardiac phase gradient signals. An example of
the measurement system 102 is described in U.S. Publication No.
2017/0119272 and in U.S. patent application Ser. No. 15/248,838,
each of which is incorporated by reference herein in its
entirety.
[0104] In some embodiments, the wide-band biopotential biophysical
signals are captured as unfiltered mammalian electrophysiological
signals such that the spectral component(s) of the signals are not
altered. Indeed, the wide-band biopotential biophysical signals are
captured, converted, and even analyzed without having been filtered
(via, e.g., hardware circuitry and/or digital signal processing
techniques, etc.) (e.g., prior to digitization) that otherwise can
affect the phase linearity of the biophysical signal of interest.
In some embodiments, the wide-band biopotential biophysical signals
are captured in in microvolt or sub-microvolt resolutions that are
at, or significantly below, the noise floor of conventional
electrocardiographic and other biophysical-signal acquisition
instruments. In some embodiments, the wide-band biopotential
biophysical signals are simultaneously sampled having a temporal
skew or "lag" of less than about 1 microseconds, and in other
embodiments, having a temporal skew or lag of not more than about
10 femtoseconds. Notably, the exemplified system minimizes
non-linear distortions (e.g., those that can be introduced via
certain filters) in the acquired wide-band phase gradient signal to
not affect the information therein.
[0105] As noted above, the measurement system 102 may be used to
capture other mammalian biopotential or electrophysiological
signals, such as, e.g., cerebral/neurological biopotential signals
or other mammalian biopotential signals associated with various
biological systems as described elsewhere herein.
[0106] Referring still to FIG. 1A, the assessment system 110 is
configured to receive the acquired biophysical-signal data set 108
(e.g., denoised, in some embodiments) (e.g., over a network) and to
generate, via a transformation operation 120 (labeled as "phase
space transformation" 120), one or more three-dimensional
vectorcardiogram data sets 122 for analysis, via, e.g., machine
learning operation and/or a predictor operation (shown as step
124), of the phase-gradient biophysical-signal data set 108.
Examples of the transformation operation and machine
learning/predictor operation is discussed below as well as in U.S.
Publication No. 2013/0096394, which is incorporated by reference
herein in its entirety.
[0107] In some embodiments, the measurement system 102 is
configured to assess the signal quality of the acquired biophysical
signal and to reject the acquired signal data set based on such
assessment. FIG. 1B is a diagram of an example system configured to
reject an acquired biophysical signal based on a quantification of
asynchronous noise and artifact contamination, in accordance with
another illustrative embodiment. In some embodiments, the
measurement system 102 is configured to perform the asynchronous
noise removal operation 118 and the signal quality assessment
operation 130 based on the quantification of the asynchronous
noise.
[0108] Because the clinical analysis of the acquired biophysical
signal 108 is performed, in some embodiments, on a separate system
(e.g., by the assessment system 110) from the measurement system
102, a signal quality check ensures that the acquired
biophysical-signal data set 108 is suitable for subsequent clinical
analysis. The near real-time operation may facilitate the prompting
of the re-acquisition of the biophysical-signal data set by the
non-invasive measurement system 102, thus, ensuring that the
acquired biophysical-signal data set is not contaminated by
asynchronous noise (such as skeletal-muscle-related noise) prior to
the biophysical-signal data set being subjected, or made available,
to further processing and analysis for a clinical assessment.
[0109] In some embodiments, the signal quality assessment operation
is performed in near real-time, e.g., less than about 1 minute or
less than about 5 minutes, to which the system can prompt for the
re-acquisition of the biophysical-signal data set. The near
real-time assessment allows the re-acquisition of the
biophysical-signal data set prior to the patient leaving the
testing room where the test is conducted.
[0110] In some embodiments, the non-invasive measurement system 102
is configured to generate a notification 126 (labeled in FIG. 1B as
"Display failed signal quality assessment" 126) of a failed or
unsuitable acquisition of biophysical-signal data set, wherein the
notification prompts that the re-acquisition of another set of the
biophysical-signal data set. The notification may be a visual
output, an audio output, or a tactile output that is provided to a
technician in proximity to the patient.
[0111] In some embodiments, the rejected biophysical-signal data
set may be stored (128) for further troubleshooting analysis (132)
of defects that led to the rejection of the acquired signal. To
this end, the rejected biophysical-signal data set is not used in
subsequent analysis (e.g., 120, 124) to yield the clinical output
112.
[0112] FIG. 1C is a diagram of an example system 100 configured to
quantify asynchronous noise such as skeletal-muscle-related
artifact noise contamination and using such quantification to
remove such contamination and/or reject an acquired biophysical
signal, in accordance with another illustrative embodiment. In FIG.
1C, the assessment system 110 is shown configured to further
pre-process (134) the received biophysical-signal data set 108 by
rejecting the received biophysical-signal data set and/or removing
the asynchronous noise from the received biophysical-signal data
set. The pre-processing operation 132 may be performed as a
substitute to, or as an additional quality operation of, the
asynchronous noise removal operation 118 (as performed on the
measurement system 102) and/or the signal-quality assessment
operation 130 (as performed on the measurement system 102).
[0113] Asynchronous Noise Removal
[0114] FIG. 2 shows an exemplary method 118 of removing
asynchronous noise from an acquired biophysical signal in
accordance with an illustrative embodiment. As shown in FIG. 2, the
method 118 includes the step of receiving (step 202), by a
processor, a biophysical-signal data set (e.g., data set 108) of a
subject 106. As noted above, the removal operation 118 can be
performed by the measurement system 102 and/or the assessment
system 110. For cardiac signals, a hand-held or other device may be
used to collect a patient's resting thoracic physiologic signals,
e.g., from a set of six probes or electrodes (e.g., probes
114a-114f), arranged along three orthogonal axes corresponding to
the X, Y, and Z channels. The electrodes as part of the
non-invasive measurement system 102 can acquire the phase-gradient
biophysical-signal data set 108 without the use of ionizing
radiation, contrast agents, exercise, or pharmacologic stressors.
The non-invasive measurement system 102, in some embodiments,
samples at about 8 kHz for a duration of between about 30 and about
1400 seconds, preferably for about 210 seconds. The acquired data
points are transferred as part of the data set 108 to the
assessment system 110 and evaluated by an analytic engine therein
employing machine-learned algorithms/predictors.
[0115] Other conventional electrode sets, and electrographic
acquisition methodologies may be used to which the method and
system disclosed herein can be applied.
[0116] Referring still to FIG. 2, the method 118 further includes
determining (step 204), by a processor, at least one
template-signal vector data set (also referred to as a
"representative vector data set") characteristic of a
representative heart-beat pattern of the subject from a plurality
of detected heart-beat cycles detected in the received cardiac
signal data set (e.g., set 108).
[0117] Referring still to FIG. 2, the method 118 further includes
applying, by a processor, the determined template-signal vector
data set to one or more denoising vector data sets. In some
embodiments, the template-signal vector data set is applied for
each of the detected cycles determined to be present in the portion
of received biophysical-signal data set (e.g., data set 108) to be
filtered. In some embodiments, the template-signal vector data set
is varied in length to match the vector length of a corresponding
detected cycle of the portion of the received biophysical-signal
data set (e.g., data set 108) to be filtered. The denoising vector
data sets collectively have a vector length corresponding to a
vector length of a portion of the received biophysical-signal data
set (e.g., data set 108) to be filtered.
[0118] Referring still to FIG. 2, the method 118 further includes
generating (step 208), by a processor, a filtered
biophysical-signal data set (also referred to as a denoised signal
data set) of the acquired biophysical signal, or a portion thereof,
by merging the portion of the received biophysical-signal data set
(e.g., data set 108) corresponding to the portion to the signal to
be filtered and the one or more generated denoising vector data
sets. In some embodiments, the merging operation is performed using
a window-based operation that applies, in the frequency domain,
weighted averages of the received biophysical-signal data set
(e.g., data set 108) and the one or more generated denoising vector
data sets.
[0119] In other embodiments, the merging operation is performed in
the time domain.
[0120] FIG. 3 is a diagram showing an example implementation method
118 of the process of FIG. 2 in accordance with an illustrative
embodiment. Method 118 includes creating (step 302), by a
processor, one or more representative cycle data set(s) each
characteristic of a representative quasi-periodic signal pattern of
the subject from a plurality of detected quasi-periodic signal
cycles detected in the received biophysical signal(s) (or dataset
associated therewith). For cardiac signals, the representative
quasi-periodic signal pattern can be characterized as a
representative heart-beat pattern. The term quasi-periodic, as used
herein, generally refers to a characteristic of a signal system
that cycles with, at a minimum, two frequency components, of which
the ratio is not a rational number. The representative cycle data
set is also referred to herein as the template-signal vector data
set. FIG. 4 is a flow diagram of an example method of
representative cycle data set in accordance with an illustrative
embodiment. FIG. 6 is a diagram of a representative cycle data set
602 (shown as 602a, 602b) characteristic of a representative
quasi-periodic pattern (e.g., representative heart-beat pattern in
an acquired cardiac signal). Discussion of FIG. 4 and FIG. 6 is
provided in subsequent sections.
[0121] Referring still to FIG. 3, step 304 to step 312 describe an
example to apply, by a processor, the template-signal vector data
set (e.g., representative cycle data set) to the one or more
denoising vector data sets (e.g., a template-signal vector data
set) to generate a denoised signal data set. As shown in FIG. 3,
step 304 includes initializing a "template vector" data set that
has a length corresponding to that of input raw signal data set.
That is, the length of the initialized template vector data set is
the same as the length of the input raw signal data set. For
example, a raw signal data set acquired over a 210-second period at
8 KHz yields via step 304 a template vector data set having a
length of 1,680,000 samples for each acquired data channel.
[0122] Step 306 includes populating, by a processor, the template
vector data set with the representative vector data set(s). That
is, in some embodiments, for each detected cycle in the raw signal
data set, method 118 includes placing or duplicating the
representative vector data set 602 in the template vector data set.
Each of the representative vector data set 602 is placed such that
a determined peak (e.g., R-peak) of the representative vector data
set 602 is aligned to a same, or similar, time-index as a
corresponding peak (e.g., R-peak) of each detected cycle.
[0123] FIG. 7 shows a diagram 700 of a method to generate, by a
processor, the template-vector data set in accordance with an
illustrative embodiment. In FIG. 7, diagram 700 includes i) a plot
of the template vector data set 702 populated with the
representative vector data set 602 (shown as 704a, 704b, 704c,
704d, 704e, and 704f) and ii) a plot of the received
biophysical-signal data set (e.g., data set 108), for a given
measurement channel, with detected cycles identified therein. As
shown in FIG. 7, the representative vector data sets (e.g.,
704a-704f) are placed/reproduced in the template vector data set
702 such that peaks (e.g., maximum peaks corresponding to R-peaks
in an acquired cardiac signal with each peak shown as 706a-706f) of
the representative vector data sets (e.g., 704a-704f) are aligned
to peaks of the biophysical-signal data set 108.
[0124] It is possible that more than one representative vector data
sets may exist with each corresponding to an assessed
quasi-periodic signal pattern (e.g., heat-beat pattern for cardiac
signals). When there are more than one representative cycle data
sets, then method 118, in some embodiments, further includes
placing a representative cycle data set selected to correspond
(i.e., more closely matches) to a given current cycle in the raw
signal data set.
[0125] Referring still to FIG. 7, when placing or reproducing the
representative cycle data set 602 in the template vector data set
702, if the representative cycle data set being placed conflicts
with existing data in the template vector data set, then the
overlapping portion (shown as 708a, 708b in FIG. 7) of the existing
data samples and the overlapping portion of the new data samples
are averaged. If there are gaps (e.g., shown as 710a, 710b) in the
template vector data set 702, then the gaps (e.g., 710a, 710b) may
be filled. by a processor. in the template vector data set with
data values determined; e.g., an optional interpolation operation
(e.g., between the last filled value and the next filled value
around the empty region), shown in FIG. 3 as step 308.
[0126] Once the template vector data set 702 has been created,
method 118 includes initiating (step 310 in FIG. 3), by a
processor, a denoising process. Step 310, in some embodiments,
includes selecting a window size of windows over which the
denoising operation is performed and a value for an interpolation
coefficient to control the influence of the template vector data
set against the raw signal data set.
[0127] In some embodiments, each window has a window size of about
0.25 seconds. In other embodiments, a window size less than about
0.25 seconds is used. In yet other embodiments, a window size
greater than about 0.25 seconds is used.
[0128] In some embodiments, a static value for the interpolation
coefficient is about 0.75 (that is, the influence attributed to the
template vector data set is about three times that of the raw
signal data set). In other embodiments, the interpolation
coefficient has a value less than about 0.75. In yet other
embodiments, the interpolation coefficient has a value greater than
about 0.75. The values of the interpolation coefficient and the
window size can be assessed based on the need to eliminate the
noise versus that to maintain signal variability. In some
embodiments, the window size or the interpolation coefficients are
allowed to vary dynamically, e.g., based on an assessment of the
signal, e.g., change with respect to an automatically quantified
level of contamination (e.g., skeletal-muscle related
contamination) in the signal.
[0129] Referring still to FIG. 3, step 310 includes initializing a
blank/null vector data set that will eventually become the denoised
signal data set 114. The initialized denoised signal vector data
set, in some embodiments, has the same number of samples as the raw
signal data set 114 of interest, that is, a same number of samples
of the raw signal 114 to be used in the subsequent machine learning
analysis.
[0130] Referring still to FIG. 3, step 312 includes creating, by a
processor, a window function for each of the windows over which to
perform the denoising operation. In some embodiments, for each
window, step 312 includes creating a Hamming window data set
centered at a middle portion of the window. When the Hamming window
data set is not placed at the exact middle sample of the signal
data set, then the Hamming window data set is placed in asymmetric
relation to the full signal data set so that samples that are equal
distances away from the middle of the windows have an equal value
in the Hamming window data set. The window function enhances, in
some embodiments, the ability of the FFT operation to extract
spectral data from signals by reducing the effects of leakage that
may occur during an FFT operation of the data. Put any way, the
window function can attenuate or remove high frequency components
that result from discontinuities in the discretization of the data
and the analysis using window. Other types of window function may
be applied, including, e.g., Hann window, Blackman window, Harris
window, sine window, Nuttall window, triangular window, and
combinations thereof, among others. Step 312 further includes
multiplying each of the raw signal data set 108 and the populated
template vector data set 702 against the window function to
generate a modified raw signal data set and a modified template
vector data set.
[0131] Referring still to FIG. 3, method 118 includes calculating
(step 314), by a processor, an envelope of the modified template
vector data set by performing a low-pass filtering operation on the
data set. In some embodiments, a Butterworth filter is used. In
some embodiments, the Butterworth filter is a 5th order filter with
a normalized cut-off frequency of 0.025. In some embodiments, a
Chebyshev filter is used.
[0132] Step 316 includes performing, by a processor, a Fast Fourier
Transform (e.g., discrete FFT) of each of the modified template
vector envelope data set and the modified raw signal data set to
transform each of them into the frequency domain.
[0133] Step 318 includes merging, by a processor, the modified
template vector envelope data set and the modified raw signal data
set in the frequency domain. In some embodiments, a weighted
average operation of the modified template vector envelope data set
and the modified raw signal data set in the frequency domain is
performed. In some embodiments, the weights used in the
interpolation are the interpolation coefficients that was initially
set to control the influence of the template vector against the raw
signal.
[0134] Step 320 includes performing, by a processor, an inverse
Fourier Transform operation to transform the resultant data back to
the time domain. The resultant data is assigned as a current window
of the denoised signal data set. The process is repeated for all
the windows, or a portion thereof, to populate the remaining
portion of the denoised signal data set.
[0135] FIG. 8 is an example plot of the raw biophysical-signal data
set 108 (e.g., shown as "Raw Signal" 108), a generated
template-vector data set 702 (e.g., shown as "Template Vector"
702), and a resulting denoised signal data set 802 (e.g., shown as
"Denoised Signal").
[0136] As shown in FIG. 8, the raw signal data set 108 is heavily
contaminated with noise (e.g., skeletal-muscle-related noise).
Application of the template vector data set 702 completely removes,
in some embodiments, that noise while maintaining some of the
high-frequency information in the QRS waveform (e.g., shown at the
notching 808 that occurs at around time index 57.8 seconds), but
template vector data set 702 does not contain most of the
variability (i.e., cardiac signal variability) present in the raw
signal data set. The denoised signal data set 802 includes such
variability (i.e., cardiac signal variability) information as in
the raw signal data set 108.
[0137] Indeed, methods described herein involve generating a
filtered cardiac signal (namely, the denoised signal) of the
cardiac signal, or a portion thereof, by merging the portion of the
received biophysical signal to be filtered (e.g., as the modified
raw signal) and the one or more generated denoising vectors (e.g.,
as the modified template vector envelope).
[0138] Determination of a Representative Cycle of a Quasi-Periodic
Signal Pattern
[0139] As noted above, FIG. 4 is a diagram of a method 400 to
determine a template-signal vector data set of a representative
cycle of a quasi-periodic signal pattern (e.g., a representative
cycle of a heart beat pattern). Method 400 may be part of an
assessment or quantification of skeletal-muscle-related artifact
and noise contamination in a biophysical signal (e.g., cardiac
signal, brain signal, etc.).
[0140] As shown in FIG. 4, the method 400 includes, first,
detecting (step 402), by a processor, peaks (e.g., R-peaks
corresponding to maximum depolarization for a cardiac signal)
across the biophysical-signal data set (e.g., data set 108), or a
portion thereof. In some embodiments, the peaks are detected using
a Pan-Tompkins algorithm, e.g., as described in Pan & Tompkins,
A Real Time QRS Detection Algorithm, IEEE Transactions on
Biomedical Engineering, Volume 32-3, 230-236, 1985, which is
incorporated by reference herein in its entirety. In other
embodiments, other algorithms to detect peaks in the cardiac signal
data set can be used. Examples include those described in Makwana
et al. "Hilbert transform based adaptive ECG R-peak detection
technique," International Journal of Electrical and Computer
Engineering, 2(5), 639 (2012); Lee et al., "Smart ECG Monitoring
Patch with Built-in R-Peak Detection for Long-Term HRV Analysis,"
Annals of Biomedical Engineering. 44(7), 2292-3201 (2016); and Kim
et al., "Detection of R-Peaks in ECG Signal by Adaptive Linear
Neuron (ADALINE)," Artificial Neural Network, presented at MATEC
Web of Conferences, 54, 10001 (2016), each of which is incorporated
by reference herein in its entirety.
[0141] In some embodiments, the system is configured to assess the
number of cycles and boundaries of the cycles in the
biophysical-signal data set (e.g., data set 108) to which subsets
of the cycles in determined groups of neighboring cycles are
subsequently used to determine template-signal vector data sets of
representative cycles. In some embodiments, the system is
configured to assess the boundaries of the cycles in the entire
biophysical-signal data set, or the portion desired to be analyzed.
In other embodiments, the system is configured to assess the
boundaries of the cycles for a pre-defined number of neighboring
cycles in the portion of the biophysical-signal data set (e.g.,
data set 108) of interest.
[0142] Neighborhood/neighboring cycles may be defined as, in some
embodiments, as .+-.1, 2, . . . 10 cycles around a middle cycle of
a set of determined cycles. In other embodiments, the
neighborhood/neighboring cycles may be defined as, +1, 2, . . . 20
cycles with respect to a beginning cycle of a set of determined
cycles. In other embodiments, the neighborhood/neighboring cycles
may be defined as -1, 2, . . . 20 cycles with respect to a last
cycle of a set of determined cycles.
[0143] To this end, multiple template-signal vector data sets may
be generated to which each template-signal vector data set is
respectively applied to the cycles used to generate it. For
example, where neighborhood group 1 is composed of cycles 1 . . .
10 and derives template vector # 1, analysis of cycles 1-10 (e.g.,
as discussed herein) are evaluated against only template vector #1;
where neighborhood group 2 is composed of cycles 5 . . . 15 (e.g.,
having some and derives template vector # 2), analysis of cycles
5-10 (e.g., as discussed herein) are evaluated against template
vector #1 and template vector #2 (e.g., by an average of vector #1
and #2); where neighborhood group 3 is composed of cycles 10 . . .
20 (e.g., having some and derives template vector # 2), analysis of
cycles 10-15 (e.g., as discussed herein) are evaluated against
template vector #2 and template vector #3 (e.g., by an average of
vector #2 and #3).
[0144] Indeed, in some embodiments, the analysis is performed until
all cardiac cycles (e.g., 3.5-minute PSR recording.times.60
BPM=210), or portions of the biophysical-signal data set of
interest, have been evaluated.
[0145] This neighborhood approach may reduce sensitivity to
long-term variation by only incorporating local cycles into the
template though may also reduce robustness to noise because fewer
component cycles are used or analyzed. Indeed, using the full
recording may capture natural cardiac variation in the entire data
set but may also create non-noise-based deviations between the
template and the test cardiac cycles. By using all of the signals
in neighborhoods, but in neighboring groups, all of the signals
(and inherent variation in the acquired signal) are still assessed
and sensitivity is locally improved.
[0146] The number of neighborhood size may be 10, e.g., as
discussed above, or it may be user-defined parameter. In some
embodiments, the neighborhood size is determined based on some
assessed variation in the signals. Indeed, the number of
neighborhood size may be 2, 3, 4, 5, 6, 7, 8, 9, 10. In some
embodiments, the number of number of neighborhood size may be
greater than 10, e.g., between 10 or 15. In some embodiments, the
number of number of neighborhood size may be greater 15, e.g.,
between 15-25. In some embodiments, the number of number of
neighborhood size may be greater 25, e.g., between 25-50.
[0147] In some embodiments, the neighborhood or groupings of cycles
are defined by an offset size. In some embodiments, the offset size
is the distance in the index count from a reference point in one
cycle to the next cycle. The reference point may be a middle point,
a beginning point, or an ending point in the cycle. In the example
above, where cycles are defined from 1 . . . 10, 5 . . . 15, 10 . .
. 20, etc., the offset size is 5 (per the reference point being at
the beginning, middle, or end).
[0148] In some embodiments, depending on the offset size and
neighborhood size, each given cycle may have one or more
template-signal vector data sets compared to it to determine a
metric (e.g., mean, median, mode, among others as discussed herein)
for that template-signal vector data set. Then the metric can be
combined to provide a revised score for that template-signal vector
data set.
[0149] For example, a template-signal vector data set may be
defined as preceding and tailing neighboring points of a reference
point defined in the middle of a given defined cycle. The
template-signal vector data set can be generated (e.g., based on
mean, mode, median, etc.) based only on the preceding and tailing
neighboring points (and not on the reference point defined in the
middle of the cycle). Once the template-signal vector data set is
generated, the template-signal vector data set is compared to the
middle of a given defined cycle to determine a score for that
score.
[0150] In some embodiments, the analysis can be iterative where the
score for subsequent cycles are combined. For example, in cycle #1,
a score #1 is determined for cycle #2. Then, for cycle #2, a score
#2 is determined based on a local score determined from only cycle
#2 and then having that local score combined with the score from
cycle #1. Then, for cycle #3, a score #3 is determined based on a
local score determined from only cycle #3 and then having that
local score combined with the score from cycles #1 and #2. This
iterative analysis can be applied for all or portion of the input
data set of interest.
[0151] Indeed, the system may choose to only apply the template to
the single cycle at, e.g., the exact center of the neighborhood. As
discussed, every single cycle is then assessed against a single
template, and that single template is unique across all the
possible templates. This type of analysis provides different
vantages of viewing local effects of the cycles.
[0152] Method 400 includes using (step 404), by a processor, the
detected peak locations to determine a median peak-to-peak interval
(e.g., median R-R peak for a cardiac signal) and to set a cycle
region around each peak (e.g., R-peak for a cardiac signal). For
cardiac signals, the cycle region is set around the R-peak and
includes both the P wave and completion of the T wave. FIG. 9 shows
a diagram of a process for a processor to segment
biophysical-signal cycles from the biophysical-signal data set to
quantify skeletal-muscle-related noise contamination in the
biophysical-signal data set. As shown in FIG. 9, the detected peak
locations (e.g., shown as 902a-902g) is used to determine a median
peak-to-peak interval (e.g., median R-R peaks for cardiac signals
as shown with 904a-904g) and to set a cycle region (e.g., shown as
906a-906f) around each peak (e.g., R-peaks for cardiac signals as
shown with 908a-908g). FIG. 9 further shows that the cycle region
is set around the R-peak and includes both the P wave (e.g., shown
as 910a-910g) and completion of the T wave (e.g., shown as
912a-912g) for a cardiac signal. In some embodiments, the ranges
are from about -20% to about +20% of the median interval (e.g.,
shown as 912a, 912b). Each of the cycle regions (e.g., 906a-906f)
can be stored by a processor in a matrix (also referred to a "cycle
matrix). The cycle matrix may be M.times.N in which M is the number
of detected cycles, and N is 40% of the median peak-to-peak
interval (e.g., median R-R intervals for cardiac signals) in which
the 40% of the peak-to-peak interval represents the full temporal
"width" of the cycle. Specifically, once the median peak-to-peak
interval (e.g., median R-R interval for cardiac signals) is known
across the dataset, the signal can be divided in half, e.g., to get
the "20%" that reaches both forward and backward in time from the
peak (e.g., R-peak) to capture the other waves (e.g., T wave and P
wave for a cardiac signal). Of course, other cycle region lengths
can be used for cardiac signals and for the various distinct waves
in brain signals, etc.
[0153] Referring to FIG. 4, method 400 includes normalizing (step
406), by a processor, each cycle to remove any offset. FIG. 10A
shows a plot of results of the normalization process of FIG. 4 in
accordance with an illustrative embodiment. In FIG. 10A, each cycle
region (e.g., 906a-906f) of the biophysical-signal data set (e.g.,
data set 108) is normalized by a processor to remove any offsets
such that the average value of each cycle region is zero. The
normalized cardiac signal data set, as shown, can have a range of
"1" and "4", though that range can vary depending on the
distribution of the data.
[0154] In some embodiments, the centering operation includes the
operation of time-aligning the same feature (e.g., peaks) among the
waveforms. Examples of these features include, for cardiac signals,
an initiation of the Q wave, a peak of the R wave, or a delay
estimate determined by a cross correlation operation, among others.
In some embodiments, the amplitude normalization operation uses
features of the QRS waveform as a basis to determine gain term
(e.g., a short average may be taken just prior to the QRS).
[0155] In other embodiments, each cycle is normalized according to
z-scores. Z-score value for a given data point in the template
signal vector data set can be calculated as a difference between
the value of the given data point and a mean of a set of cycles in
which the difference is then normalized by the standard deviation
of that given data point to the same indexed data value of the set
of cycles. In some embodiments, the z-score may be outputted as a
cycle variability score. Cycle variability may refer to the degree
of variability between cycles in an acquired biophysical data set
that may be attributed to asynchronous noise, among others.
[0156] Referring still to FIG. 4, method 400 further includes
performing, by a processor, a principal component analysis (PCA) on
the generated cycle matrix to extract the first two principal
components. FIG. 11 shows an example output of a principal
component analysis operation performed on the generated cycle
matrix.
[0157] Referring still to FIG. 4, method 400 further includes
performing (step 410), by a processor, a clustering operation on
the output of the principal component analysis. An example of a
clustering operation that can be used includes the DBSCAN algorithm
as described in Ester, Kriegel, Sander, Xu, "A density-based
algorithm for discovering clustering in large spatial databases
with noise," Proceedings of the Second International Conference on
Knowledge Discover and Data Mining. Pages 226-231, which is
incorporated by reference herein in its entirety. In some
embodiments, the clustering operation is configured to be performed
on the first two PCA components, which, in some embodiments,
represent the cycles in a two-dimensional space. If the algorithm
detects a second dominant cluster representing more than 10% of the
total number of cycles, then that signifies the presence of a
second dominant cycle morphology, such as premature ventricular
contractions. It is noted that FIG. 11 does not contain multiple
distinct cycle morphologies, per it's identification by DBSCAN. The
data set visually appears to have two levels due to the level of
EMG in the signal.
[0158] Referring still to FIG. 4, method 400 includes extracting
(step 412), by a processor, a representative cycle based on all, or
some of, the cycles that correspond to the dominant PCA cluster;
e.g., as detected by DBSCAN. The representative cycle may be
extracted in one or several ways, each with different
characteristics. In some embodiments, each of the data points in
the representative cycle will embody an underlying distribution,
where that distribution is composed that time-point in all the M
cycles. For example, taking the mean (across all M points, for each
N) has a low-pass filtering effect (removing both high-frequency
information and noise), while taking the median preserves
high-frequency information in a non-linear fashion. The differing
impact of the compression technique, mean vs. median, is accounted
for by varying underling distributions. If the M points are
normally distributed, then the mean and median have the same
result, but start to differ with more complex distributions, such
as those with non-zero skewness, and especially combination with
negative kurtosis, or in the presence of multimodality.
[0159] As noted above, FIG. 6 shows example mean representative
cycle (e.g., 602a) and median representative cycle (e.g., 602b) for
the same underlying cycles. It is observed that the fragmentation
(e.g., a high-frequency content that is preserved in the median
cycle, but removed in the mean cycle, e.g., as shown at arrow 604)
is preserved in the median cycle between 3000-3500 samples, while
the mean cycle has removed that feature (604). Additionally, some
high-frequency noise is visible in the median beat, but not the
mean beat, throughout the representative cycle. Using the median
operation preserves high frequency features that may not be present
in the mean representation due to changes in QRS morphology over
time or because of time smearing associated with beat detection
alignment. In addition, functions to describe distributions may be
used; such functions would create spectral masks that can remove or
enhance characteristics that are desired for removal or
preservation (such as, for example, the mode of the kernel density
estimate of the underlying distribution).
[0160] Put another way, the mean beat can be used to generate a
"cleaner" representation of the cycle (i.e., less high-frequency
content, where that high-frequency content includes both signal and
noise characteristics), whereas the median beat contains that
high-frequency content. Either one of these approaches may be more
desired depending on the situation. For example, the median beat
may be used when i) it is desired to ensure that the high-frequency
component of the biophysical signal characteristics is captured and
maintained for analysis even if there are some high-frequency noise
present that could cloud the analysis or ii) there is little or low
high-frequency noise in the signal.
[0161] The process of FIG. 4 of determining a representative cycle
of a quasi-periodic signal cycle can be a part of a large study to
quantify skeletal-muscle-related noise and other asynchronous noise
contamination in an acquired biophysical signal.
[0162] In some embodiments, portions of the resulting windows that
are neighbors within a set of windows are combined and assessed
(e.g., to generate the template signal or to reject a signal). FIG.
10B shows a template-signal vector data set superimposed on top of
a set of stacked cycles for a high-noise signal. FIG. 10C shows a
template-signal vector data set superimposed on top of a set of
stacked cycles for a low-noise signal. FIG. 10A is based on a
single PSR recording, and FIG. 10B is based on a second single PSR
recording.
[0163] Once is cycle is identified (e.g., in each of these cases),
the identified cycles can be stacked (i.e., plotted or arranged on
top of each other). For example, cycle 1 data point 1 is placed at
x=1 and cycle 1 data point 6000 is placed at x=6000; then cycle 150
(for example) data point 1 is also placed at x=1 and cycle 150 data
point 6000 is also placed at x=6000.
[0164] In FIGS. 10B and 10C, once the data are stacked, a
template-signal vector data set 702 corresponding to a template for
a given cycle can be generated.
[0165] Notably, FIGS. 10B and 10C demonstrate intermediate outputs
of an embodiment. FIG. 10B shows that that this technique is
capable of extracting a meaningful template vector in the presence
of very high noise (where the typical cycle isn't otherwise
visually obvious), while FIG. 10C shows that the technique is able
to extract the typical cycle under ideal conditions.
[0166] Quantification of Skeletal-muscle Artifact Noise
Contamination in a Biophysical Signal
[0167] FIG. 5 is a diagram of an example method 500 to quantify, by
a processor, skeletal-muscle-related artifact noise contamination
in an acquired biophysical signal in accordance with an
illustrative embodiment.
[0168] Method 500 includes steps 402-412 as discussed in relation
to FIG. 4 and further includes the step of quantifying, by a
processor, the distribution of differences between the determined
representative cycle data set and the raw signal data set(s).
[0169] Method 500 further includes comparing each detected cycle in
the raw signal data set cycle to the representative cycle data set.
The comparison is performed by, first, phase-aligning (step 502)
the representative cycle with each of the cycles under examination.
In some embodiments, a method such as finding the maximum of the
cross-correlation is used.
[0170] The comparison further includes determining (step 504) a
difference between the representative cycle data set and the
phase-aligned cycle under examination. In some embodiments, a
method such as correlation between the two signals is used. In
other embodiments, a median absolute error is used. In yet other
embodiments, a mean absolute error is used. If there is more than
one representative cycle data set (as, e.g., detected through
clustering on the two-dimensional PCA output), then corresponding
representative cycle data set that most match a given cycle is
used.
[0171] The comparison further includes differentiating (step 506)
outlying cycles and inlying cycles based on a difference score
determined, e.g., using a distribution-based filter. In some
embodiments, the distribution-based filter is configured to
identify cycles having a standard-deviation greater than one from
the mean. FIG. 12 is a plot of a distribution of difference scores
determined based on a comparison of the representative cycle data
set and each of the evaluated cycles as a function of cycle index.
As shown in FIG. 12, the inlying cycles are identified (e.g., in
the region denoted by 1202) to be within one standard deviation of
the mean of the distribution (shown as line 1204), and the outlying
cycles are identified (e.g., in the regions denoted by 1206) to be
outside the one standard deviation region from the mean. A final
assessment of the contamination of the biophysical signal by the
skeletal-muscle-related noise can be performed by taking a
representative value of the inlying difference scores, such as the
mean or the median.
[0172] Without wishing to be bound to a particular theory, the
presence of outlying cycles can be attributed to several factors,
including noise introduced by physiological variability of the
biophysical signals and underlying physiological system under
study. For cardiac signals, the outlying cycles may be due to
variability in the length and/or energy of depolarization or
repolarization cycles, among others.
[0173] Discussion
[0174] As noted above, quantification of asynchronous noise
contamination such as skeletal-muscle-related artifact and noise
contamination in a biophysical signal (such as a cardiac signal)
can be complex. Skeletal-muscle-related artifact and noise, for
example, can appear as in-band noise with respect to the
biophysical signal--that is, it can occur in the same frequency
range as the dominant components of the biophysical signal,
typically around 0.5 Hz-80 Hz for cardiac signals and around 0.1-50
Hz for brain signals. Further, EMG can also have a similar
amplitude as typical cardiac or brain waveform.
[0175] Similarity of skeletal-muscle-related artifact and noise
contamination to the biophysical signal can cause issues for
automated diagnostic analysis of such signals, and therefore,
quantifying the level of skeletal-muscle-related artifact and noise
contamination in a signal can facilitate the automated rejection of
signals that are likely to be unsuccessful in subsequent analyses
and/or the compensation for such contamination in subsequent
analyses.
[0176] When quantifying the level of skeletal-muscle-related
artifact and noise in a biophysical signal, particularly for
cardiac signals, it is observed that skeletal-muscle-related
artifact and noise is not in synchrony with the biophysical signal.
Because the sources of the two are different (i.e., whereby the
cardiac signal is derived from the summation of the action
potentials of the cardiac myocytes, while the EMG is derived from
the summation of the action potentials of the originating muscle
(such as the pectoral muscles, biceps, triceps, etc.)), the sources
are unlikely to share a deeper common source that could create
synchronicity. Indeed, skeletal-muscle-related artifact and noise
can be quantified by comparing each cardiac cycle to the idealized
cardiac cycle for that patient in which the gross differences can
be accounted by the presence of skeletal-muscle-related artifact
and noise contamination in the biophysical signal.
[0177] In the same way that skeletal-muscle-related artifact and
noise quantification is a problem (e.g., skeletal-muscle-related
artifact and noise being in-band with the physical signal), so is
the challenge of skeletal-muscle-related artifact and noise
denoising.
[0178] By leveraging the same insight from skeletal-muscle-related
artifact and noise quantification, a time-series data set of the
representative cycles can be generated to which a frequency-based
analysis or time-based analysis can be performed to remove, or
reduce, the skeletal-muscle-related artifact and noise and other
asynchronous contamination.
[0179] Indeed, in some embodiments, a sample-by-sample comparison
of the original signal in the frequency domain can be performed
followed by a frequency domain denoising operation between the
signals to derive the denoised signals based on a spectral mask
determined from the representative cycle vector and using that to
mask noise features in the original signal in frequency domain. The
exemplified denoising approach leverages the robust information
contained in the representative cycle along with the information on
the variation of the biophysical signal contained in the raw
data.
[0180] The exemplified methods and systems is demonstrated above in
relation to cardiac signals. It is noted that exemplified methods
and systems can be applied to brain signals and other biophysical
signals. FIGS. 13A, 13B, and 13C show an example wide-band cerebral
phase gradient signal data set acquired from the measurement system
102 of FIG. 1A. FIG. 14 illustrates the wide-band cerebral phase
gradient signals of FIGS. 13A-13C presented in phase space. Indeed,
the wide-band cerebral phase gradient signal is a quasi-periodic
system and is similar to a cardiac wide-band cardiac phase gradient
signal in that regard, to which the exemplified methods and systems
can be applied.
[0181] Device Normalization Process
[0182] In another aspect, the asynchronous contamination removal
operation as described herein can be used to normalize cardiac
signals acquired from multiple and different acquisition platforms;
e.g., prior to subjecting data acquired from such platform for
machine-learning-based disease association. The normalization is
driven, at least in part, by knowledge of theoretical topological
differences and insights from deep learning. The device
normalization process can be applied to data acquired from multiple
acquisition devices, e.g., that are from two or more different
generations to increase similarity (as guided by both machine
learning and electrical engineering theory) between the groups of
signals that can improve the machine learning training process.
[0183] FIG. 15 is a diagram of a method to normalize a first set of
data sets acquired with a first set of biophysical-signal
measurement equipment and a second set of data sets acquired with a
second set of biophysical-signal measurement equipment such that
the first set of data sets may be analyzed with the second set of
data sets in a machine learning operation. As shown in FIG. 15, a
first set of cardiac signal data sets (e.g., shown as 1502) of a
first set of subjects (e.g., shown as 1504) is acquired with a
first set of biophysical-signal measurement equipment (e.g., shown
as 1506), and a second set of cardiac signal data sets (e.g., shown
as 1508) of a second set of subjects (e.g., shown as 1510) is
acquired with a second set of biophysical-signal measurement
equipment (e.g., shown as 1512).
[0184] In some embodiments, the first set of cardiac signal data
sets (e.g., 1502) is processed with a processor to remove
asynchronous noise contamination as described in relation to FIG. 1
so as to improve the similarity between the first set of cardiac
signal data sets and the second set of cardiac signal data sets and
to facilitate the use of the first set of cardiac signal data sets
and the second set of cardiac signal data sets in a same training
data set for a machine learning operation.
[0185] In some embodiments, the second set of cardiac signal data
sets (e.g., 1508) is processed with a processor to remove
asynchronous noise contamination as described in relation to FIG.
1A so as to improve the similarity between the first set of cardiac
signal data sets and the second set of cardiac signal data
sets.
[0186] Experimental Results
[0187] A Coronary Artery Disease--Learning Algorithm Development
(CADLAD) study was undertaken involving two distinct stages to
support development and testing of machine-learned algorithms. In
stage 1, paired clinical data were used to guide the design and
development of the pre-processing, feature extraction and machine
learning steps. That is, the collected clinical study data is split
into three cohorts: Training (50%), validation (25%), and
verification (25%). Similar to the steps described above for
processing signals from a patient for analysis, each signal is
first pre-processed, to clean and normalize the data. Following
these processes, a set of features are extracted from the signals
in which each set of features is paired with a representation of
the true condition--for example, the binary classification of the
presence or absence of significant CAD. The final output of this
stage is a fixed algorithm embodied within a measurement system. In
Stage 2 of the CADLAD study, the machine-learned algorithms will be
used to provide a determination of significant CAD against a pool
of previously untested clinical data. The criteria for disease is
established as that defined in the American College of Cardiology
(ACC) clinical guidelines, specifically as greater than 70%
stenosis by angiography or less than 0.80 fraction-flow by flow
wire.
[0188] For part of the study, a first set of cardiac signal data
sets associated with an earlier acquisition hardware (e.g., "Gen
1") (e.g., measurement system 104) is processed to remove the
asynchronous noise contamination as described in relation to FIG.
1A to facilitate of use of the first set of cardiac signal data
sets and a second set of the cardiac signal data sets (acquired
with a later acquisition hardware, e.g., "Gen 2") (e.g.,
measurement system 104) as training data set for the machine
learning operation in the CADLAD study. Further description of an
example earlier acquisition hardware (e.g., comprising a unipolar
sensing front end) can be found in U.S. Publication No.
2017/0119272, which is incorporated by reference herein in its
entirety, and further description of a later acquisition hardware
(e.g., comprising a bipolar sensing front end) can be found in U.S.
application Ser. No. 15/911,047, which is also incorporated by
reference herein in its entirety.
[0189] The assessment system 110, in some embodiments,
automatically and iteratively explores combinations of features in
various functional permutations with the aim of finding those
combinations which can successfully match a prediction based on the
features. To avoid overfitting of the solutions to the training
data, the validation set is used as a comparator. Once candidate
predictors have been developed, they are then manually applied to a
verification data set to assess the predictor performance against
data that has not been used at all to generate the predictor.
Provided that the data sets are sufficiently large, the performance
of a selected predictor against the verification set will be close
to the performance of that predictor against new data.
[0190] FIG. 16 is an example method 1600 of generating and
analyzing a phase space volumetric object 122 by the non-invasive
cardiac assessment system 110 in accordance with an implementation
of the present disclosure. Other implementations may become evident
to one of ordinary skill in the art based on this disclosure. The
method 1600 includes, in some embodiments, removing (operation
1602) a baseline wander from the raw differential channel signal of
phase-gradient biophysical-signal data set 108. In some
implementations, the raw differential channel signal is derived
from six signals simultaneously sampled by the measurement system
102.
[0191] In some implementations, six simultaneously sampled signals
are captured from a resting subject as the raw differential channel
signal data set in which the signals embed the inter-lead timing
and phase information of the acquired signals, specific to the
subject. Geometrical contrast arising from the interference in the
phase plane of the depolarization wave with the other orthogonal
leads can be used which can facilitate superimposition of phase
space information on a three-dimensional representation of the
heart. Noiseless subspaces further facilitate the observation of
the phase of these waves. That is, the phase of the orthogonal
leads carries the information about the structure and generates
geometrical contrast in the image. Phase-contrast takes advantage
of the fact that different bioelectric structures have different
impedances, and so spectral and non-spectral conduction delays and
bends the trajectory of phase space orbit through the heart by
different amounts. These small changes in trajectory can be
normalized and quantified beat to beat and corrected for abnormal
or poor lead placement, and the normalized phase space integrals
can be mapped to a geometric mesh for visualization.
[0192] In some implementations, the raw differential channel signal
data set is normalized, and baseline wander are removed using a
modified moving average filter. For example, in some
implementations, the baseline wander is extracted from each of the
raw differential channel signals using a median filter with an
order of 1500 milliseconds, smoothed with a 1-Hz low-pass filter,
and subtracted from the signals. The bias is then removed from the
resulting signals by subtracting estimations of the signals using
maximums of probability densities calculated with a kernel
smoothing function. All of the signals, or a portion thereof, may
be divided by their respective interquartile ranges to complete the
normalization process.
[0193] The method 1600 then includes, in some embodiments,
reconstructing (operation 1604) a noiseless model signal by
decomposing and selecting sets of candidate basis functions to
create a sparse mathematical model. In some implementations, a
Modified Matching Pursuit (MMP) algorithm is used to find a
noiseless model of the raw differential channel signals. Other
sparse approximation algorithms can be used, including, and not
limited to, evolvable mathematical models, symbolic regression,
orthogonal matching pursuit, LASSO, linear models optimized using
cyclical coordinate descent, orthogonal search, fast orthogonal
search, and cyclical coordinate descent. In some implementations,
the reconstructing operation 504 generates a model as a function
with a weighted sum of basis functions in which basis function
terms are sequentially appends to an initially empty basis to
approximate a target function while reducing the approximation
error.
[0194] The method 1600 then includes, in some embodiments,
selecting (operation 506) subspace components (e.g., low energy
frequency subspace components) from the selected basis functions
and coefficients. The low-energy subspace components comprise a
model reconstructed by using only the X% low magnitude subset
coefficients (frequency content) contributing least to the
modelling error. Low-energy subspace components, in some
implementations, includes higher order candidate terms that are
later selected, in the phase space coordinates, as part of the
sparse representation of a signal. That is, the last 5 percent, 10
percent, 15 percent, 20 percent, 25 percent, 30 percent of the
candidate terms (as the higher order candidate terms) last selected
via the sparse approximation is used. Other percentage values can
be used.
[0195] The method 1600 then includes, in some embodiments,
reconstructing (operation 1608) a pre-defined set of n.sup.th order
fractional-calculus result set (e.g., via a numeric
fractional-calculus operation) to generate a three-dimensional
point cloud defining, in part, the phase space volumetric object
122. In some implementations, the fractional-calculus operation is
based on Grunwald-Letnikov fractional-derivative method. In some
implementations, the fractional derivative operation is based on
the Lubich's fractional linear multi-step method. In some
implementations, the fractional-calculus operation is based on the
fractional Adams-Moulton method. In some implementations, the
fractional-calculus operation is based on the Riemann-Liouville
fractional derivative method. In some implementations, the
fractional derivative operation is based on Riesz fractional
derivative method. Other methods of performing a fractional
calculus may be used.
[0196] The method 1600 then includes, in some implementations,
performing (1610) triangulation operation to generate surface
features (i.e., face) of the point cloud. In some implementations,
Alpha Hull triangulation with a pre-predetermined radius (a) is
performed on the reconstructed noiseless model signals. In other
implementations, Delaunay triangulation, alpha shapes, ball
pivoting, Mesh generation, Convex Hull triangulation, and the like,
is used.
[0197] The method 1600 then includes, in some implementations,
computing (1612) one or more values for each of the vertices in the
point cloud. The vertex values, in some implementations, are scaled
over a presentable color range. The vertex values, in some
implementations, is a variance between a modeled channel data set
(e.g., X-axis data set, Y-axis data set, or Z-axis data set) a
base-line raw channel data set (e.g., corresponding X-axis data
set, Y-axis data set, or Z-axis data set). In some implementations,
the variance is determined by subtracting data points of the
base-line raw channel data set with the corresponding data points
of the modeled channel data set. The modeled channel data set, in
some implementations, is based on a sparse approximation of the
base-line raw channel data set to generate a reconstructed
noiseless signal of the base-line raw channel data. In some
implementations, each face of the phase space volumetric object 122
is assigned a face color value triangularly interpolated among
neighboring bounding vertex color values (e.g., 3 bounding vertex
colors).
[0198] In some implementations, various views of the phase space
volumetric object 122 are captured for presentation as computed
phase space tomographic images, e.g., via a web portal, to a
physician to assist the physician in the assessment of presence or
non-presence of pulmonary arterial hypertension. In some
implementations, the phase space volumetric object or the computed
phase space tomographic images are assessed by a trained neural
network classifier configured to assess for presence or
non-presence of pulmonary arterial hypertension. In some
implementations, the computed tomographic images are presented
(e.g., a set of two-dimensional views) alongside the results of a
machine-generated predictions to assist in the physician in making
a diagnosis.
[0199] In other implementations, the phase space volumetric object
122 is analyzed in subsequent machine learning operations (e.g.,
image-based machine learning operations or feature-based machine
learning operations) to determine the one or more coronary
physiological parameters. In some implementations, the assessment
system 110 is configured to determine a volume metric (e.g., alpha
hull volume) of the phase space volumetric object 122. In some
implementations, the assessment system 110 is configured to
determine a number of distinct bodies (e.g., distinct volumes) of
the generated phase space volumetric object 122. In some
implementations, the assessment system 110 is configured to assess
a maximal color variation (e.g., color gradient) of the generated
phase space volumetric object 122. In some implementations, all
these features are assessed from phase space volumetric object 122
as a mathematical feature.
[0200] In some implementations, the mathematical features of the
phase space volumetric object 122 are extracted along with hundreds
of other distinct mathematical features that represent specific
aspects of the biophysical signals collected. A feature extraction
engine of the assessment system 110 may extract each feature as a
specific formula/algorithm. In some implementations, when the
feature extraction process is applied to an incoming biophysical
signal, the output is a matrix of all calculated features which
includes a list, for example, of over hundreds of real numbers; one
number per feature in which each feature represents one or more
aspects of the signal's dynamical, geometrical, fractional
calculus, chaotic, and/or topological properties.
[0201] A machine learning algorithm (e.g., meta-genetic algorithm),
in some implementations, is used to generate predictors linking
aspects of the phase space model (e.g., pool of features) across a
population of patients representing both positive (i.e., have
disease) and negative (i.e., do not have disease) cases to detect
the presence of myocardial tissue associated with pulmonary
arterial hypertension. In some implementations, the performances of
the candidate predictors are evaluated through a verification
process against a previously unseen pool of patients. In some
implementations, the machine learning algorithm invokes a
meta-genetic algorithm to automatically select a subset of features
drawn from a large pool. This feature subset is then used by an
Adaptive Boosting (AdaBoost) algorithm to generate predictors to
diagnose pulmonary arterial hypertension across a population of
patients representing both positive and negative cases. The
performances of the candidate predictors are determined through
verification against a previously unseen pool of patients. A
further description of the AdaBoost algorithm is provided in
Freund, Yoav, and Robert E. Schapire, "A decision-theoretic
generalization of on-line learning and an application to boosting,"
European conference on computational learning theory. Springer,
Berlin, Heidelberg (1995), which is incorporated by reference
herein in its entirety.
[0202] In some implementations, the assessment system 110 generates
one or more images of a representation of the phase space
volumetric object 122 in which the vertices, face triangulations,
and vertex colors are presented. In some implementations, multiple
views of the representation are generated and included in a report.
In some implementations, the one or more images are presented as a
three-dimensional object that can be rotated, scaled, and/or panned
based on user's inputs. Indeed, such presentation can be used to be
assessed visually by a skilled operator to determine whether a
subject has presence of non-presence of pulmonary arterial
hypertension.
[0203] Neural Network Classification
[0204] The three-dimensional phase-space volumetric object or the
computed phase-space tomographic images can be directly evaluated
by a trained neural network classifier to determine presence or
non-presence of pulmonary arterial hypertension. In some
implementations, the neural network classifier may be a neural
network trained on a set of grayscale tomographic images which are
paired with coronary angiography results assessed for presence and
non-presence of pulmonary arterial hypertension. In some
implementations, a neural network-based nonlinear classifier is
used. In some implementations, the neural network-based non-linear
classifier is configured to map individual pixels from the
generated tomographic images to a binary disease-state prediction
(i.e., the condition exists or does not exist) or an estimated
physiological characteristic. In some implementations, the neural
network's weights, which govern this mapping, is optimized using
gradient descent techniques.
[0205] Examples of a disease state prediction can include, but not
limited to, presence/non-presence of significant coronary arterial
disease, presence/non-presence of pulmonary hypertension,
presence/non-presence of pulmonary arterial hypertension,
presence/non-presence of pulmonary hypertension due to left heart
disease, presence/non-presence of pulmonary hypertension due to
lung disease, presence/non-presence of pulmonary hypertension due
to chronic blood clots, etc.
[0206] Examples of an estimated physiological characteristic can
include, but not limited to, fractional flow reserve, degree of
stenosis, degree of ischemia, blood glucose levels, cardiac chamber
size and mechanical function, etc.
[0207] Further examples of processing that may be used with the
exemplified method and system are described in: U.S. Pat. No.
9,289,150, entitled "Non-invasive Method and System for
Characterizing Cardiovascular Systems"; U.S. Pat. No. 9,655,536,
entitled "Non-invasive Method and System for Characterizing
Cardiovascular Systems"; U.S. Pat. No. 9,968,275, entitled
"Non-invasive Method and System for Characterizing Cardiovascular
Systems"; U.S. Pat. No. 8,923,958, entitled "System and Method for
Evaluating an Electrophysiological Signal"; U.S. Pat. No.
9,408,543, entitled "Non-invasive Method and System for
Characterizing Cardiovascular Systems and All-Cause Mortality and
Sudden Cardiac Death Risk"; U.S. Pat. No. 9,955,883, entitled
"Non-invasive Method and System for Characterizing Cardiovascular
Systems and All-Cause Mortality and Sudden Cardiac Death Risk";
U.S. Pat. No. 9,737,229, entitled "Noninvasive Electrocardiographic
Method for Estimating Mammalian Cardiac Chamber Size and Mechanical
Function"; U.S. Pat. No. 10,039,468, entitled "Noninvasive
Electrocardiographic Method for Estimating Mammalian Cardiac
Chamber Size and Mechanical Function"; U.S. Pat. No. 9,597,021,
entitled "Noninvasive Method for Estimating Glucose, Glycosylated
Hemoglobin and Other Blood Constituents"; U.S. Pat. No. 9,968,265,
entitled "Method and System for Characterizing Cardiovascular
Systems From Single Channel Data"; U.S. Pat. No. 9,910,964,
entitled "Methods and Systems Using Mathematical Analysis and
Machine Learning to Diagnose Disease"; U.S. Patent Publication No.
2017/0119272, entitled "Method and Apparatus for Wide-Band Phase
Gradient Signal Acquisition"; PCT Publication No. WO2017/033164,
entitled "Method and Apparatus for Wide-Band Phase Gradient Signal
Acquisition"; U.S. Patent Publication No. 2018/0000371, entitled
"Non-invasive Method and System for Measuring Myocardial Ischemia,
Stenosis Identification, Localization and Fractional Flow Reserve
Estimation"; PCT Publication No. WO2017/221221, entitled
"Non-invasive Method and System for Measuring Myocardial Ischemia,
Stenosis Identification, Localization and Fractional Flow Reserve
Estimation"; U.S. Pat. No. 10,292,596, entitled "Method and System
for Visualization of Heart Tissue at Risk"; U.S. patent application
Ser. No. 16/402,616, entitled "Method and System for Visualization
of Heart Tissue at Risk"; U.S. Patent Publication No. 2018/0249960,
entitled "Method and System for Wide-band Phase Gradient Signal
Acquisition"; U.S. patent application Ser. No. 16/232,801, entitled
"Method and System to Assess Disease Using Phase Space Volumetric
Objects"; PCT Application No. IB/2018/060708, entitled "Method and
System to Assess Disease Using Phase Space Volumetric Objects";
U.S. Patent Publication No. US2019/0117164, entitled "Methods and
Systems of De-Noising Magnetic-Field Based Sensor Data of
Electrophysiological Signals"; U.S. patent application Ser. No.
16/232,586, entitled "Method and System to Assess Disease Using
Phase Space Tomography and Machine Learning"; PCT Application No.
PCT/IB2018/060709, entitled "Method and System to Assess Disease
Using Phase Space Tomography and Machine Learning"; U.S. patent
application Ser. No. __/______, entitled "Method and System to
Assess Disease Using Phase Space Tomography and Machine Learning"
(having attorney docket no. 10321-034pv1 and claiming priority to
U.S. Patent Provisional Application No. 62/784,984); U.S. patent
application Ser. No. 16/429,593, entitled "Method and System to
Assess Pulmonary Hypertension Using Phase Space Tomography and
Machine Learning"; U.S. patent application Ser. No. __/______,
entitled "Method and System for Automated Quantification of Signal
Quality" (having attorney docket no. 10321-036pv1 and claiming
priority to U.S. Patent Provisional Application No. 62/784,962);
U.S. patent application Ser. No. __/______, entitled "Method and
System to Configure and Use Neural Network To Assess Medical
Disease" (having attorney docket no. 10321-037pv1 and claiming
priority to U.S. Patent Provisional Application No. 62/784,925);
U.S. patent application Ser. No. __/______, entitled "Method and
System to Assess Disease Using Phase Space Volumetric Object and
Machine Learning" (having attorney docket no. 10321-038pv1 and
claiming priority to U.S. Patent Provisional Application No.
62/785,158); U.S. patent application Ser. No. 15/653,433, entitled
"Discovering Novel Features to Use in Machine Learning Techniques,
such as Machine Learning Techniques for Diagnosing Medical
Conditions"; U.S. patent application Ser. No. 15/653,431, entitled
"Discovering Genomes to Use in Machine Learning Techniques"; U.S.
Provisional Application No. __/______, filed concurrently herewith
(having attorney docket no. 10321-041pv1), entitled "Method and
System to Assess Disease Using Dynamical Analysis of Cardiac and
Photoplethysmographic Signals", each of which is incorporated by
reference herein in its entirety.
[0208] Unless otherwise expressly stated, it is in no way intended
that any method set forth herein be construed as requiring that its
steps be performed in a specific order. Accordingly, where a method
claim does not actually recite an order to be followed by its steps
or it is not otherwise specifically stated in the claims or
descriptions that the steps are to be limited to a specific order,
it is no way intended that an order be inferred, in any respect.
This holds for any possible non-express basis for interpretation,
including: matters of logic with respect to arrangement of steps or
operational flow; plain meaning derived from grammatical
organization or punctuation; the number or type of embodiments
described in the specification.
[0209] While the methods and systems have been described in
connection with certain embodiments and specific examples, it is
not intended that the scope be limited to the particular
embodiments set forth, as the embodiments herein are intended in
all respects to be illustrative rather than restrictive.
[0210] The methods, systems and processes described herein may be
used generate stenosis and FFR outputs for use in connection with
procedures such as the placement of vascular stents within a vessel
such as an artery of a mammalian (e.g., human) subject, and other
interventional and surgical system or processes. In one embodiment,
the methods, systems and processes described herein can be
configured to use the FFR/stenosis outputs to determine and/or
modify, intra operation, a number of stents to be placed in a
mammalian (e.g., human), including their optimal location of
deployment within a given vessel, among others.
[0211] Examples of other biophysical signals that may be analyzed
in whole, or in part, using the exemplary methods and systems
include, but are not limited to, an electrocardiogram (ECG) data
set, an electroencephalogram (EEG) data set, a gamma synchrony
signal data set; a respiratory function signal data set; a pulse
oximetry signal data set; a perfusion data signal data set; a
quasi-periodic biological signal data set; a fetal ECG data set; a
blood pressure signal; a cardiac magnetic field data set, and a
heart rate signal data set.
[0212] The exemplary analysis can be used to identify various
pathologies and condition including, but are not limited to heart
disease, cardiac arrhythmia, diabetic autonomic neuropathy,
Parkinson's disease, forms of epilepsy, brain injury, altered state
of cognition, stability of a heart at different heart rates,
effectiveness of medication, ischemic, silent ischemia, atrial
fibrillation, ventricular fibrillation, ventricular tachycardia,
blood vessel block, pulmonary hypertension, attention deficit
disorder, etc.
* * * * *