Abstract
Stethoscopic auscultation is still one of the primary tools for the diagnosis of heart diseases due to its easy accessibility and relatively low cost. Recently, many research efforts have been done on the automatic classification of heart sound signals for supporting clinicians to make better heart sound diagnosis. Conventionally, automatic classification methods of the heart sound signals have been usually based on artificial neural networks (ANNs). But, in this paper, we propose to use hidden Markov models (HMMs) as the classification tool for the heart sound signal. In the experiments classifying 10 different kinds of heart sound signals, the proposed method has shown quite successful results compared with ANNs achieving average classification rate about 99%.
This work has been supported by The Advanced Medical Technology Cluster for Diagnosis and Prediction at KNU, which carries out one of the R&D Projects sponsored by the Korea Ministry Of Commerce, Industry and Energy.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chung, YJ. (2006). A Classification Approach for the Heart Sound Signals Using Hidden Markov Models. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_41
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DOI: https://doi.org/10.1007/11815921_41
Publisher Name: Springer, Berlin, Heidelberg
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