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:
Jul. 06, 2021

Filed:

Aug. 30, 2019
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Rohan Banerjee, Kolkata, IN;

Sakyajit Bhattacharya, Kolkata, IN;

Soma Bandyopadhyay, Kolkata, IN;

Aniruddha Sinha, Kolkata, IN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/025 (2006.01); A61B 5/316 (2021.01); A61B 5/024 (2006.01); A61B 5/25 (2021.01); A61B 5/00 (2006.01); A61B 5/021 (2006.01);
U.S. Cl.
CPC ...
A61B 5/316 (2021.01); A61B 5/02405 (2013.01); A61B 5/25 (2021.01); A61B 5/02108 (2013.01); A61B 5/7239 (2013.01); A61B 5/7264 (2013.01);
Abstract

Electrocardiography (ECG) signals contain important markers for Coronary Heart Disease (CHD). State of the art systems and methods rely on clinically available multi-lead ECG for CHD classification which is not cost effective. Moreover the state of the art methods are applied on digital ECG time series data only. Also, discriminative HRV markers are not often present in short ECG recordings necessitating long hours of ECG data to analyze. In accordance with the present disclosure, systems and methods described hereinafter extract ECG time series from ECG images obtained from commercially available low-cost single lead ECG devices through a combination of image and signal processing steps including Histogram analysis, Morphological operation-thinning, Extraction of lines, Extraction of Reference Pulse, Extraction of ECG and interpolating missing data. Further, domain independent statistical features such as self-similarity of raw ECG time series and average Maharaj's distance along with domain specific features are used for classifying CHD.


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