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. 28, 2022

Filed:

Mar. 06, 2018
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Deepan Das, Kolkata, IN;

Rohan Banerjee, Kolkata, IN;

Anirban Dutta Choudhury, Kolkata, IN;

Parijat Dilip Deshpande, Pune, IN;

Nital Shah, Pune, IN;

Vijay Anil Date, Pune, IN;

Arpan Pal, Kolkata, IN;

Kayapanda Muthana Mandana, Kolkata, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G08B 21/04 (2006.01); G16H 50/20 (2018.01); A61B 5/00 (2006.01); A61B 7/04 (2006.01); G06N 20/00 (2019.01); A61B 7/00 (2006.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); A61B 5/725 (2013.01); A61B 5/7214 (2013.01); A61B 5/7267 (2013.01); A61B 7/00 (2013.01); A61B 7/04 (2013.01); G06N 20/00 (2019.01);
Abstract

A system and method for classifying the phonocardiogram (PCG) signal quality has been described. The system is configured to identify the quality of the PCG signal recording and accepting only diagnosable quality recordings for further cardiac analysis. The system includes the derivation of plurality features of the PCG signal from the training dataset. The extracted features are preprocessed and are then ranked using mRMR algorithm. Based on the ranking the irrelevant and redundant features are rejected if their mRMR strength is less. A training model is generated using the relevant set of features. The PCG signal of the person under test is captured using a digital stethoscope and a smartphone. The PCG signal is preprocessed and only the relevant set of features are extracted. And finally the PCG signal is classified into diagnosable or non-diagnosable using the relevant set of features and a random forest classifier.


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