Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method is able to recognize the murmur efficiently and superior to BP neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.
 S. Molau, M. Pitz, R. Schluter and H. Ney, “Computing Mel-Frequency Cepstral Coeffcients on the Power Spectrum,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Utah, 7-11 May 2001, pp. 73-76.