Non-invasive method and system for characterizing cardiovascular systems for all-cause mortality and sudden cardiac death risk

• US 10,383,535 B2
• Filed: 03/30/2018
• Issued: 08/20/2019
• Est. Priority Date: 08/17/2012
• Status: Active Grant

• Current Assignee: Analytics For Life, Inc.
• Original Assignee: Analytics For Life, Inc.
• Inventors: Gupta, Sunny, Yazdi, Mohsen Najafi, Burton, Timothy William Fawcett, Ramchandani, Shyamlal, Exner, Derek Vincent
• Primary Examiner(s): Mahmood, Nadia A
• Application Number:
  US15/941,736
• Publication Number:
  US 20180325403A1
• Time in Patent Office: Days
• Field of Search: 600512
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/14552 Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/344 Foetal cardiography
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• 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 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.

Claims

1. A method for quantifying the risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, comprising:

obtaining, by one or more processors, an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle; and

processing the obtained electrophysiological data set to detect at least one patterns that predict a risk of sudden cardiac death, other modes of death, and all-cause mortality, the processing being performed without the use of data from other measuring devices or invasive procedures, the obtained electrophysiological data set including at least 12 dimensional dynamical phase space densities,

wherein the at least one risk of sudden cardiac death, other modes of death, and all-cause mortality, or values associated therewith, is presented for clinical utility.

2. The method of claim 1,

wherein the obtained electrophysiological data set comprises at least one heart beat cycle,

wherein the at least one patterns can be detected within a predetermined time interval within the cardiac cycle, wherein the at least one cardiac cycle corresponds to a vector sum electrical activation pathway through the heart, and the vector sum electrical activation pathway is used with time information associates with the at least 12 dimensional phase space densities to determine the at least one patterns that predict the risk of sudden cardiac death, other modes of death and all-cause mortality,

and wherein the predetermined time interval is at least 50 seconds and typically from 100 to 700 seconds.

3. The method of claim 1, further comprising using at least 12 variables corresponding to the at least 12 dimensional dynamical phase space densities as terms that are selected in nonlinear combinations selected from a list comprising sin, cos, cos h, sin h, Rossler functions, product, division, addition, subtraction, Gaussian, exponential functions and become candidates based on the genetic operators selected from a second list comprising inheritance, mutation, selection, and crossover.

4. The method of claim 3, further comprising:

using the at least 12 variables in a genetic algorithm; and

modeling, using the at least 12 variables to link sudden cardiac death risk, other modes of death and all-cause mortality, to the obtained electrophysiological data set.

5. The method of claim 1, wherein the at least 12 dimensional dynamical phase space densities from the heart can be machine learned based on mining and linking of the obtained electrophysiological data set to associated patient outcomes.

6. The method of claim 5, further comprising associating other biomarkers to the at least 12 dimensional dynamical phase space densities.

7. The method of claim 6, further comprising associating an effectiveness of patient therapies that includes using the model results to guide a treatment or intervention.

8. The method of claim 1, further comprising modeling the at least 12 dimensional dynamical phase space densities on a reconstructed 3D model of the heart to visually display a location of the abnormality in the heart and to display a potential value of the abnormality that is arrhythmogenic.

9. The method of claim 1, further comprising extracting measures of cardiac output including ejection fraction by mining and linking the obtained electrophysiological data set to associated patient measures of ejection fraction based on other cardiac imaging modalities.

10. A system for quantifying the risk of serious arrhythmias, sudden cardiac death, or other modes of death and all-cause mortality events in mammals, comprising:

a non-invasive system configured to measure electrical properties of a heart over at least one heart beat cycle as an electrophysiological data set,

wherein the electrophysiological data set is processed to detect patterns that predict a risk of sudden cardiac death, other modes of death, and all-cause mortality, the processing being performed without the use of data from other measuring devices or invasive procedures, the obtained electrophysiological data set including at least 12 dimensional dynamical phase space densities, and

wherein the at least one risk of sudden cardiac death, other modes of death, and all-cause mortality, or values associated therewith, is presented for clinical utility.

11. The system of claim 10, wherein the predetermined time interval is at least 50 seconds and typically from 100 to 700 seconds.

12. The system of claim 10, wherein execution of the instructions by the one or more processors, further cause the one or more processors to:

use at least 12 variables corresponding to the at least 12 dimensional dynamical space densities as terms that are selected in nonlinear combinations selected from a list comprising sin, cos, cos h, sin h, Rossler functions, product, division, addition, subtraction, Gaussian, exponential functions and become candidates based on the genetic operators selected from a second list comprising inheritance, mutation, selection, and crossover.

13. The system of claim 12, wherein execution of the instructions by the one or more processors, further cause the one or more processors to:

use the at least 12 variables in a genetic algorithm; and

model, using the at least 12 variables to link sudden cardiac death risk, other modes of death and all-cause mortality, to the obtained electrophysiological data set.

14. The system of claim 10, wherein the at least 12 dimensional dynamical phase space densities from the heart can be machine learned based on mining and linking of the obtained electrophysiological data set to associated patient outcomes.

15. The system of claim 14, wherein execution of the instructions by the one or more processors, further cause the one or more processors to:

associate other biomarkers to the at least 12 dimensional dynamical phase space densities.

16. The system of claim 15, wherein execution of the instructions by the one or more processors, further cause the one or more processors to:

associate an effectiveness of patient therapies that includes using the model results to guide a treatment or intervention.

17. The system of claim 10, wherein execution of the instructions by the one or more processors, further cause the one or more processors to:

model the at least 12 dimensional dynamical phase space densities on a reconstructed 3D model of the heart to visually display a location of the abnormality in the heart and to display a potential value of the abnormality to be arrhythmogenic.

18. The system of claim 10, further comprising extracting measures of cardiac output including ejection fraction by mining and linking the obtained electrophysiological data set to associated patient measures of ejection fraction based on other cardiac imaging modalities.

19. The system of claim 10, wherein the risk of other modes of death is selected from the group consisting of a risk for serious heart rhythm disturbances, a risk for all-cause mortality linked to abnormalities of the heart, a risk associated with heart failure, a risk associated with ischemia, a risk associated with coronary artery disease, and a risk associated with cardiac arrhythmia.

20. A non-transitory computer readable medium comprising instructions stored thereon, wherein execution of the instructions by one or more processors, cause the one or more processors to:

obtain an electrophysiological data set associated with a measurement of an electrophysiological signal having been acquired from a noninvasive system configured to measure electrical properties of a heart over at least one heart beat cycle; and

process the obtained electrophysiological data set to detect patterns that predict a risk of sudden cardiac death, other modes of death, and all-cause mortality, the processing being performed without the use of data from other measuring devices or invasive procedures, the obtained electrophysiological data set including at least 12 dimensional dynamical phase space densities,

wherein the at least one risk of sudden cardiac death, other modes of death, and all-cause mortality, or values associated therewith, is presented for clinical utility.