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:
Feb. 01, 2022

Filed:

Jan. 12, 2018
Applicant:

Shanghai Lepu Cloudmed Co., Ltd, Shanghai, CN;

Inventors:

Chang Liu, Beijing, CN;

Chuanyan Hu, Beijing, CN;

Weiwei Zhou, Beijing, CN;

Haitao Lu, Beijing, CN;

Jiayu Wang, Beijing, CN;

Jun Cao, Beijing, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/316 (2021.01); A61B 5/00 (2006.01); A61B 5/352 (2021.01); A61B 5/364 (2021.01); A61B 5/366 (2021.01);
U.S. Cl.
CPC ...
A61B 5/316 (2021.01); A61B 5/352 (2021.01); A61B 5/364 (2021.01); A61B 5/366 (2021.01); A61B 5/7203 (2013.01); A61B 5/7267 (2013.01); A61B 5/7271 (2013.01);
Abstract

A self-learning dynamic electrocardiography analysis method employing artificial intelligence. The method comprises: pre-processing data, performing cardiac activity feature detection, interference signal detection and cardiac activity classification on the basis of a deep learning method, performing signal quality evaluation and lead combination, examining cardiac activity, performing analytic computations on an electrocardiogram event and parameters, and then automatically outputting report data. The method achieves an automatic analysis method for a quick and comprehensive dynamic electrocardiography process, and recording of modification information of an automatic analysis result, while also collecting and feeding back modification data to a deep learning model for continuous training, thereby continuously improving and enhancing the accuracy of the automatic analysis method. Also disclosed is a self-learning dynamic electrocardiography analysis device employing artificial intelligence.


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