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:
Dec. 22, 2020

Filed:

Dec. 05, 2018
Applicant:

General Electric Company, Schenectady, NY (US);

Inventors:

Weijia Lu, Shanghai, CN;

Joel Xue, Wauwatosa, WI (US);

Shuyan Gu, Shanghai, CN;

Jie Shuai, Shanghai, CN;

Lifei Hu, Beijing, CN;

Assignee:

General Electric Company, Schenectady, NY (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/024 (2006.01); A61B 5/046 (2006.01); A61B 5/00 (2006.01); G06N 3/08 (2006.01); A61B 5/0464 (2006.01); A61B 5/0245 (2006.01); A61B 5/0468 (2006.01); A61B 5/04 (2006.01);
U.S. Cl.
CPC ...
A61B 5/046 (2013.01); A61B 5/0245 (2013.01); A61B 5/02405 (2013.01); A61B 5/0464 (2013.01); A61B 5/7264 (2013.01); A61B 5/7275 (2013.01); G06N 3/08 (2013.01); A61B 5/0006 (2013.01); A61B 5/04012 (2013.01); A61B 5/0468 (2013.01); A61B 5/7257 (2013.01);
Abstract

A system for identifying arrhythmias based on cardiac waveforms includes a storage system storing a trained deep neural network system, wherein the trained deep neural system includes a trained representation neural network and a trained classifier neural network. A processing system is communicatively connected to the storage system and configured to receive cardiac waveform data for a patient, identify a time segment in the cardiac waveform data, and transform the time segment into a spectrum image. The processing system is further configured to generate, with the representation neural network, a latent representation from the spectrum image, and then to generate, with the classifier neural network, an arrhythmia classifier from the latent representation.


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