A Classification Approach for the Heart Sound Signals Using Hidden Markov Models
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%.
KeywordsHide Markov Model Viterbi Decode Heart Sound Signal Average Classification Rate Normal Sound
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