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Feature Extraction Based on Mel-Scaled Wavelet Transform for Heart Sound Analysis

To provide a robust representation of heart sound signal in an automatic heart disease diagnosis system, a mel-scaled wavelet transform has been developed. It combines the advantages of linear perceptual scale by mel mapping with the suitability of analyzing non-stationary signals by wavelet transform. The heart sound signal is firstly divided into windowing frames. A set of mel-scaled filterbank is then used to calculate the mel-warped spectrum, which is followed by a wavelet transform to extract the mel-scaled wavelet features. The performance of the proposed algorithm is evaluated for heart sound analysis using clean and noisy data, and compared with the standard mel-frequency cepstral coefficients. Results show that the proposed approach provides a robust representation of heart sound signal in noisy environment