# US 10,441,216 B2 · Method and system for characterizing cardiovascular systems from single channel data

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- Number: US 10,441,216 B2
- Document ID: US10441216B2
- Status: Granted
- Kind: B2
- Application number: 15/926,290
- Filed: 2018-03-20
- Published: 2018-07-26
- Granted: 2019-10-15
- Earliest claimed: 2012-08-17
- Assignees: Analytics For Life Inc.
- Applicants: Analytics for Life
- Inventors: Timothy Burton; Shyamlal Ramchandani; Matthew Howe-Patterson; Mohsen Yazdi; Sunny Gupta
- Web page: https://patentgrep.com/patent/US10441216B2

## CPC

- A61B 5/4848: {Monitoring or testing the effects of treatment, e.g. of medication}
- A61B 5/02028: {Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction}
- A61B 5/33: specially adapted for cooperation with other devices
- A61B 5/341: Vectorcardiography [VCG]
- A61B 5/364: Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
- A61B 5/6804: {Garments; Clothes}
- A61B 5/681: {Wristwatch-type devices}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/50: for simulation or modelling of medical disorders
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- A61B 5/7203: {for noise prevention, reduction or removal}
- A61B 5/7267: {involving training the classification device}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

Methods to identify and risk stratify disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and other toxic agents, pathological substrates, conduction delays and defects, and ejection fraction using single channel biological data obtained from the subject. A modified Matching Pursuit (MP) algorithm may be used to find a noiseless model of the data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to characterize the cardiac system. In another method, space-time domain is divided into a number of regions (which is largely determined by the signal length), the density of the signal is computed in each region and input to a learning algorithm to associate them to the desired cardiac dysfunction indicator target.

## Claims

### Claim 1

A method for detecting a cardiac dysfunction using a computing device, comprising:
receiving only one-dimensional electrophysiological data;
constructing multi-dimensional electrophysiological data from the only one-dimensional electrophysiological data;
defining a phase space system from the multi-dimensional electrophysiological data;
detecting the cardiac dysfunction from the phase space system; and
providing an indicator of the cardiac dysfunction.

### Claim 2 (depends on 1)

The method of claim 1, wherein the step of constructing multi-dimensional electrophysiological data comprises:
denoising the one-dimensional electrophysiological data to generate denoised one-dimensional data;
removing a baseline of the denoised one-dimensional electrophysiological data; and
taking fractional derivatives of the denoised one-dimensional electrophysiological data to construct the multi-dimensional electrophysiological data.

### Claim 3 (depends on 1)

The method of claim 1, wherein the one-dimensional electrophysiological data comprises at least one cardiac cycle.

### Claim 4 (depends on 3)

The method of claim 3, wherein the cardiac cycle corresponds to a vector sum of electrical activation of the heart.

### Claim 5 (depends on 4)

The method of claim 4, wherein the step of detecting the cardiac dysfunction from the phase space system further comprises:
using variables from an N dimensional space-time information to determine feature vectors; and
correlating the feature vectors with attributes that are associated with the cardiac dysfunction.

### Claim 6 (depends on 5)

The method of claim 5, wherein the step of correlating is performed using one or more machine learning algorithms.

### Claim 7 (depends on 6)

The method of claim 6, wherein the one or more machine learning algorithms includes a genetic algorithm mediated map generation or artificial neural network.

### Claim 8 (depends on 5)

The method of claim 5, further comprising:
dividing the N dimensional space-time information into a predetermined number of regions;
determining a density of a time series of each region; and
using the density of each region in the step of correlating.

### Claim 9 (depends on 1)

The method of claim 1, wherein the cardiac dysfunction is at least one of disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and toxic agents, pathological substrates, conduction delays and defects, and ejection fraction.

### Claim 10 (depends on 1)

The method of claim 1, wherein the step of providing an indicator of the cardiac dysfunction further comprises generating a 3-D phase space plot containing the indicator.

### Claim 11 (depends on 10)

The method of claim 10, wherein the indicator is a complex subharmonic frequency (CSF) trajectory that is associated with the cardiac dysfunction.

### Claim 12 (depends on 1)

The method of claim 1, further comprising the step of receiving the one-dimensional electrophysiological data from one of a one-dimensional recorder, an implantable telemeter, a smartphone, a smart handheld consumer device, a smart watch, a perfusion sensor, and clothing embedded with biometrics sensors.

### Claim 13

A method for detecting a cardiac dysfunction using a computing device, comprising:
receiving only one-dimensional electrophysiological data;
constructing multi-dimensional electrophysiological data from the only one-dimensional electrophysiological data;
defining a phase space system from the multi-dimensional electrophysiological data;
determining by a baseline measurement of a subject's physiological condition from the phase space system;
determining a first risk score associated with the baseline measurement of the subject's physiological condition;
applying a therapy to the subject and subsequently receiving only second one-dimensional electrophysiological data; constructing second multi-dimensional electrophysiological data from the only second one-dimensional electrophysiological data; defining a second phase space system from the second multi-dimensional electrophysiological data;
determining a second measurement of the subject's physiological condition from the second phase space system after the therapy is applied;
determining a second risk score associated with the second measurement of the subject's physiological condition; and
comparing the first risk score and the second risk score to determine a utility of patient therapies.

### Claim 14

A non-transitory computer readable medium having computer readable instructions stored thereon that when executed by a computing device causes the computing device to perform a method for detecting a cardiac dysfunction, the method comprising:
receiving only one-dimensional electrophysiological data;
constructing multi-dimensional electrophysiological data from the only one-dimensional electrophysiological data;
defining a phase space system from the multi-dimensional electrophysiological data;
detecting the cardiac dysfunction from the phase space system; and
providing an indicator of the cardiac dysfunction.

### Claim 15 (depends on 14)

The non-transitory computer readable medium of claim 14, further comprising instructions for:
denoising the one-dimensional electrophysiological data to generate denoised one-dimensional data;
removing a baseline of the denoised one-dimensional electrophysiological data; and
constructing the multi-dimensional electrophysiological data by taking fractional derivatives of the denoised one-dimensional electrophysiological data.

### Claim 16 (depends on 14)

The non-transitory computer readable medium of claim 14, further comprising instructions for:
using variables from an N dimensional space-time information to determine feature vectors; and
correlating the feature vectors with attributes that are associated with the cardiac dysfunction.

### Claim 17 (depends on 16)

The non-transitory computer readable medium of claim 16, further comprising instructions for:
dividing the N dimensional space-time information into a predetermined number of regions;
determining a density of a time series of each region; and
using the density of each region in the step of correlating.

### Claim 18 (depends on 14)

The non-transitory computer readable medium of claim 14, further comprising instructions for generating a 3-D phase space plot containing the indicator.

### Claim 19 (depends on 14)

The non-transitory computer readable medium of claim 14, wherein the cardiac dysfunction is at least one of disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and toxic agents, pathological substrates, conduction delays and defects, and ejection fraction.

### Claim 20 (depends on 14)

The non-transitory computer readable medium of claim 14, wherein the step of detecting of the cardiac dysfunction is performed using at least one machine learning algorithm.