Intrinsic mode decomposition of physiological signals for feature extraction

B. Samanta, C. Nataraj

Research output: Contribution to book or proceedingConference articlepeer-review

Abstract

This paper presents a study for extracting features from physiological signals using intrinsic mode decomposition. The complex, nonlinear and non-stationary biomedical signals are first decomposed into intrinsic mode functions (IMF). Next each IMF is subjected to morphological signal processing (MSP) for extracting features, namely, pattern spectrum entropy (PSEn), that characterize the shape-size complexity of the component signals. These along with other features like energy (E) and sample entropy (SampEn) are extracted from the individual IMF as well as the cumulative sums of IMF for characterizing the signals. The procedure is illustrated using heart sound signals digitally recorded during cardiac auscultation representing different cardiac conditions. The study examines the effectiveness of IMF based features in the assessment of cardiac state.

Original languageEnglish
Title of host publicationProceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
Pages233-239
Number of pages7
EditionPART A
DOIs
StatePublished - 2010
Event2009 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2009 - San Diego, CA, United States
Duration: Aug 30 2009Sep 2 2009

Publication series

NameProceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
NumberPART A
Volume1

Conference

Conference2009 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2009
Country/TerritoryUnited States
CitySan Diego, CA
Period08/30/0909/2/09

Scopus Subject Areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Keywords

  • Biomedical signal processing
  • Empirical mode decomposition
  • Mathematical morphology
  • Pattern spectrum
  • Sample entropy

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