Classification of respiratory sounds by using an artificial neural network

M. C. Sezgin, Z. Dokur*, T. Ölmez, M. Korürek

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

Original languageEnglish
Pages (from-to)697-699
Number of pages3
JournalAnnual Reports of the Research Reactor Institute, Kyoto University
Volume1
Publication statusPublished - 2001
Event23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey
Duration: 25 Oct 200128 Oct 2001

Keywords

  • Artificial neural network
  • Classification of biomedical signals
  • Respiratory sounds

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