Method and system for characterizing cardiovascular systems from single channel data

• US 9,968,265 B2
• Filed: 02/12/2015
• Issued: 05/15/2018
• Est. Priority Date: 08/17/2012
• Status: Active Grant

• Current Assignee: Analytics For Life
• Original Assignee: Analytics For Life
• Inventors: Burton, Timothy, Ramchandani, Shyamlal, Howe-Patterson, Matthew, Yazdi, Mohsen, Gupta, Sunny
• Primary Examiner(s): LEVICKY, WILLIAM J
• Application Number:
  US14/620,388
• Publication Number:
  US 20150216426A1
• Time in Patent Office: 1,188 Days
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: 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
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

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

1. A method for detecting a cardiac dysfunction, comprising:

obtaining only a single channel of electrophysiological data for a heart;

processing solely the single channel of electrophysiological data by a computing device to construct multi-dimensional biological data by taking a fractional derivative of the electrophysiological data to define a phase space system; and

detecting the cardiac dysfunction from the phase space system to enable presentation of an indication of the cardiac dysfunction in a 3-D phase space plot.

2. The method of claim 1, wherein the single channel of electrophysiological data comprises at least one cardiac cycle and wherein the cardiac dysfunction is detected within a predetermined time interval within the cardiac cycle.

3. The method of claim 2, wherein the predetermined time interval is about 10 seconds, and wherein the predetermined time interval is used in the step of detecting the cardiac dysfunction.

4. The method of claim 2, wherein the cardiac cycle corresponds to a vector sum of electrical activation of the heart, and a pathway is used with N dimensional space density time information in the step of detecting the cardiac dysfunction.

5. The method of claim 4, wherein variables from the N dimensional space density time information become feature vectors, and wherein the feature vectors are indicator target attributes that are associated with the cardiac dysfunction using machine learning algorithms.

6. The method of claim 5, wherein the machine learning algorithms include a genetic algorithm mediated map generation or artificial neural network.

7. The method of claim 5, further comprising determining a utility of patient therapies.

8. The method of claim 1, wherein the method identifies at least one of disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and other toxic agents, pathological substrates, conduction delays and defects, and ejection fraction in a mammalian heart.

9. The method of claim 1, further comprising obtaining the single channel of electrophysiological data from one of a single channel recorder, an implantable telemeter, a smartphone or other smart handheld consumer devices, a smart watch, a perfusion sensor, and clothing embedded with biometrics sensors.