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. 28, 2023

Filed:

Dec. 01, 2017
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Arijit Ukil, Kolkata, IN;

Soma Bandyopadhyay, Kolkata, IN;

Chetanya Puri, Kolkata, IN;

Rituraj Singh, Kolkata, IN;

Arpan Pal, Kolkata, IN;

Debayan Mukherjee, Kolkata, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/024 (2006.01); G16H 50/20 (2018.01); G06F 16/783 (2019.01); A61B 34/10 (2016.01); A61B 7/04 (2006.01); H04R 29/00 (2006.01);
U.S. Cl.
CPC ...
A61B 5/02405 (2013.01); A61B 5/02444 (2013.01); A61B 7/04 (2013.01); A61B 34/10 (2016.02); G06F 16/7834 (2019.01); G16H 50/20 (2018.01); H04R 29/00 (2013.01);
Abstract

This disclosure relates generally to physiological monitoring, and more particularly to feature set optimization for classification of physiological signal. In one embodiment, a method for physiological monitoring includes identifying clean physiological signal training set from an input physiological signal based on a Dynamic Time Warping (DTW) of segments associated with the physiological signal. An optimal features set is extracted from a clean physiological signal training set based on a Maximum Consistency and Maximum Dominance (MCMD) property associated with the optimal feature set that strictly optimizes on the objective function, the conditional likelihood maximization over different selection criteria such that diverse properties of different selection parameters are captured and achieves Pareto-optimality. The input physiological signal is classified into normal signal components and abnormal signal components using the optimal features set.


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