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:
Jun. 25, 2024

Filed:

Sep. 30, 2020
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Rohan Banerjee, Kolkata, IN;

Avik Ghose, Kolkata, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/02 (2006.01); A61B 7/04 (2006.01); G06N 3/00 (2023.01); G06N 3/02 (2006.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 3/088 (2023.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01);
U.S. Cl.
CPC ...
A61B 5/7282 (2013.01); A61B 5/02028 (2013.01); A61B 5/725 (2013.01); A61B 5/7257 (2013.01); A61B 5/7267 (2013.01); A61B 5/7278 (2013.01); A61B 7/04 (2013.01); G06N 3/00 (2013.01); G06N 3/02 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 3/088 (2013.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01);
Abstract

The disclosure generally relates to methods and systems for identifying presence of abnormal heart sounds from heart sound signals of a subject being monitored. Conventional Artificial intelligence (AI) based abnormal heart sounds detection models with supervised learning requires a substantial amount of accurate training datasets covering all heart disease types for the training, which is quiet challenging. The present methods and systems solve the problem solves the problem of identifying presence of the abnormal heart sounds using an efficient semi-supervised learning model. The semi-supervised learning model is generated based on probability distribution of spectrographic properties obtained from heart sound signals of healthy subjects. A Kullback-Leibler (KL) divergence between a predefined Gaussian distribution and an encoded probability distribution of the semi-supervised learning model is determined as an anomaly score for identifying the abnormal heart sounds.


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