Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization

Leena Pesu, Panu Helistö, E. Ademovic, J.-C. Pesquet, Ari Saarinen, A. Sovijärvi

    Research output: Contribution to journalArticleScientificpeer-review

    34 Citations (Scopus)

    Abstract

    In this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.
    Original languageEnglish
    Pages (from-to)65-74
    JournalTechnology and Health Care
    Volume6
    Issue number1
    DOIs
    Publication statusPublished - 1998
    MoE publication typeA1 Journal article-refereed

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