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:
Mar. 03, 2020

Filed:

Mar. 06, 2018
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Shahnawaz Alam, Kolkata, IN;

Shreyasi Datta, Kolkata, IN;

Anirban Dutta Choudhury, Kolkatta, IN;

Arpan Pal, Kolkata, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/024 (2006.01); A61B 5/145 (2006.01); A61B 5/1455 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7221 (2013.01); A61B 5/02416 (2013.01); A61B 5/02438 (2013.01); A61B 5/1455 (2013.01); A61B 5/14542 (2013.01); A61B 5/6898 (2013.01); A61B 5/7203 (2013.01); A61B 5/7246 (2013.01); A61B 5/7267 (2013.01);
Abstract

This disclosure relates generally to PPG signal quality assessment, and more particularly to, a system and method for sensor agnostic PPG signal quality assessment using morphological analysis. In one embodiment, a method for PPG signal quality assessment includes obtaining a PPG signal captured using a testing device in real-time, and segmenting into a first plurality of PPG signal samples such that length of each of the first plurality of PPG signal samples more than a threshold length. A signal sufficiency check (SSC) is performed for each first PPG signal sample to obtain at least a first set of PPG signal samples complying with the SSC. A set of features is extracted from the first set of PPG signal samples, based on which each PPG signal sample is identified as one of a noisy and clean signal sample using a plurality of Random Forest (RF) models created during the training phase.


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