US 11,883,180 B2
Electrocardiographic identification of non-ST elevation ischemic events
Salah Al-Zaiti, Monroeville, PA (US); Ervin Sejdic, Pittsburgh, PA (US); and Clifton W. Callaway, Pittsburgh, PA (US)
Assigned to University of Pittsburgh—Of the Commonwealth System of Higher Education, Pittsburgh, PA (US)
Filed by University of Pittsburgh—Of the Commonwealth System of Higher Education, Pittsburgh, PA (US)
Filed on Oct. 14, 2020, as Appl. No. 17/070,530.
Application 17/070,530 is a continuation of application No. 15/562,196, granted, now 10,820,822, previously published as PCT/US2016/027105, filed on Apr. 12, 2016.
Claims priority of provisional application 62/146,775, filed on Apr. 13, 2015.
Prior Publication US 2021/0100469 A1, Apr. 8, 2021
Int. Cl. A61B 5/36 (2021.01); A61B 5/366 (2021.01); A61B 5/00 (2006.01); A61B 5/02 (2006.01); A61B 5/30 (2021.01); A61B 5/316 (2021.01); A61B 5/363 (2021.01); A61B 5/364 (2021.01); A61B 5/282 (2021.01)
CPC A61B 5/366 (2021.01) [A61B 5/02007 (2013.01); A61B 5/303 (2021.01); A61B 5/316 (2021.01); A61B 5/363 (2021.01); A61B 5/364 (2021.01); A61B 5/6823 (2013.01); A61B 5/282 (2021.01)] 17 Claims
OG exemplary drawing
 
1. An electrocardiographic (ECG) method comprising:
receiving ECG data related to a person;
determining that the person lacks ST elevation (STE) based on the ECG data;
determining one or more spatial or temporal qualities of ventricular repolarization dispersion (VRD) from the ECG data for the person, the one or more spatial or temporal qualities of VRD including a T wave complexity ratio;
using machine learning to combine the one or more spatial or temporal qualities of VRD to indicate a likelihood of non-ST elevation myocardial infarction (NSTEMI) in the person.