Automatic phonocardiograph signal analysis for detecting heart valve disorders

Wen-Chung Kao, Chih Chao Wei

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

Skilled cardiologists probe heart sounds by electronic stethoscope through human ears, but interpretations of heart sounds is a very special skill which is quite difficult to teach in a structured way. Because of this reason, automatic heart sound analysis in computer systems would be very helpful for medical staffs. This paper presents a complete heart sound analysis system covering from the segmentation of beat cycles to the final determination of heart conditions. The process of heart beat cycle segmentation includes autocorrelation for predicting the cycle time of a heart beat. The feature extraction pipeline includes stages of the short-time Fourier transform, the discrete cosine transform, and the adaptive feature selection. Many features are extracted, but only a few specific ones are selected for the classification of each hyperplane based on a systematic approach. The experiments are done by a public heart sound database released by Texas Heart Institute. A very promising recognition rate has been achieved.

Original languageEnglish
Pages (from-to)6458-6468
Number of pages11
JournalExpert Systems with Applications
Volume38
Issue number6
DOIs
Publication statusPublished - 2011 Jun 1

Fingerprint

Signal analysis
Acoustic waves
Feature extraction
Discrete cosine transforms
Autocorrelation
Fourier transforms
Computer systems
Pipelines

Keywords

  • 2-D discrete cosine transform
  • Adaptive feature extraction
  • Phonocardiogram (PCG)
  • Short-time Fourier transform
  • Support vector machines (SVMs)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Cite this

Automatic phonocardiograph signal analysis for detecting heart valve disorders. / Kao, Wen-Chung; Wei, Chih Chao.

In: Expert Systems with Applications, Vol. 38, No. 6, 01.06.2011, p. 6458-6468.

Research output: Contribution to journalArticle

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