The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 01, 2018

Filed:

Jul. 11, 2016
Applicant:

Analytics for Life, Ganaoque, CA;

Inventors:

Sunny Gupta, Amherstview, CA;

Mohsen Najafi Yazdi, Kingston, CA;

Timothy William Fawcett Burton, Ottawa, CA;

Shyamlal Ramchandani, Kingston, CA;

Derek Vincent Exner, Calgary, CA;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/04 (2006.01); A61B 5/0205 (2006.01); A61B 5/021 (2006.01); A61B 5/029 (2006.01); A61B 5/044 (2006.01); A61B 5/0468 (2006.01); A61B 5/1455 (2006.01); A61B 5/0452 (2006.01); A61B 5/026 (2006.01); A61B 5/0444 (2006.01); A61B 5/08 (2006.01);
U.S. Cl.
CPC ...
A61B 5/04012 (2013.01); A61B 5/021 (2013.01); A61B 5/0205 (2013.01); A61B 5/026 (2013.01); A61B 5/029 (2013.01); A61B 5/04001 (2013.01); A61B 5/044 (2013.01); A61B 5/04011 (2013.01); A61B 5/0444 (2013.01); A61B 5/0452 (2013.01); A61B 5/0468 (2013.01); A61B 5/08 (2013.01); A61B 5/14551 (2013.01); A61B 5/14552 (2013.01); A61B 5/4836 (2013.01); A61B 5/7275 (2013.01);
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.


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