Automatic segmentation of heart sound signals using hidden Markov models

A. D. Ricke, R. J. Povinelli, M. T. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

49 Scopus citations

Abstract

The monitoring of respiration rates using impedance plethysmography is often confused by cardiac activity. This paper proposes using the phonocardiogram as an alternative, since the process of respiration affects heart sounds. As part of this research, a technique is developed to segment heart sounds into its component segments, using Hidden Markov Models. The heart sounds data is preprocessed into feature vectors, where the feature vectors are comprised of the average Shannon energy of the heart sound signal, the delta Shannon energy, and the delta-delta Shannon energy. The performance of the segmentation system is validated using eight-fold cross-validation.

Original languageEnglish
Title of host publicationComputers in Cardiology, 2005
Pages953-956
Number of pages4
DOIs
StatePublished - 2005
EventComputers in Cardiology, 2005 - Lyon, France
Duration: Sep 25 2005Sep 28 2005

Publication series

NameComputers in Cardiology
Volume32
ISSN (Print)0276-6574

Conference

ConferenceComputers in Cardiology, 2005
Country/TerritoryFrance
CityLyon
Period9/25/059/28/05

ASJC Scopus subject areas

  • Computer Science Applications
  • Cardiology and Cardiovascular Medicine

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