US 12,011,276 B2
Non-invasive method and system for measuring myocardial ischemia, stenosis identification, localization and fractional flow reserve estimation
Sunny Gupta, Beleville (CA); Shyamlal Ramchandani, Kingston (CA); Timothy William Fawcett Burton, Toronto (CA); William Sanders, Bethseda, MD (US); and Ian Shadforth, Morrisville, NC (US)
Assigned to Analytics For Life Inc., Toronto (CA)
Filed by Analytics For Life Inc., Toronto (CA)
Filed on Aug. 16, 2021, as Appl. No. 17/402,743.
Application 17/402,743 is a continuation of application No. 16/524,475, filed on Jul. 29, 2019, granted, now 11,089,988.
Application 16/524,475 is a continuation of application No. 15/633,330, filed on Jun. 26, 2017, granted, now 10,362,950, issued on Jul. 30, 2019.
Claims priority of provisional application 62/409,176, filed on Oct. 17, 2016.
Claims priority of provisional application 62/354,673, filed on Jun. 24, 2016.
Prior Publication US 2021/0369170 A1, Dec. 2, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 5/316 (2021.01); A61B 5/00 (2006.01); A61B 5/02 (2006.01); A61B 5/026 (2006.01); A61B 5/282 (2021.01); A61B 5/1455 (2006.01)
CPC A61B 5/316 (2021.01) [A61B 5/02007 (2013.01); A61B 5/026 (2013.01); A61B 5/282 (2021.01); A61B 5/726 (2013.01); A61B 5/0006 (2013.01); A61B 5/14551 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for detecting one or more pathologies of a human subject, the system comprising:
an analysis system configured with computer readable instructions to retrieve an electrophysiological signal data set that (i) is associated with the subject and (ii) has been acquired by measuring equipment configured to non-invasively capture, via one or more sensors, the signal data set as a long gradient record that exhibits complex non-linear variability, the analysis system using the signal data set to detect the one or more pathologies of the subject,
wherein detecting the one or more pathologies comprises:
generating, by a processor executing a transformation operation, an intermediate phase space data set from the signal data set; and
determining, by the processor, one or more geometric features and/or dynamical properties of the intermediate phase space data set, wherein the geometric features and/or the dynamical properties include at least one of:
a 3D volume value,
a void value,
a surface area value, and
a principal curvature direction value,
wherein the determined one or more geometric features and/or dynamical properties of the intermediate phase space data set are used as variables representative of the subject in a machine learning operation to detect the one or more pathologies of the subject.