Wavelet packet based features for insufficient murmur identification

This paper presents a new analysis method for aortic and mitral insufficient murmurs using wavelet packet decomposition. We proposed four diagnostic features including the maximum peak frequency, the position index of the wavelet packet coefficient corresponding to the maximum peak frequency, and the ratios of the wavelet energy and entropy information to achieve greater accuracy for detection of heart murmurs. The proposed wavelet packet based insufficient murmur analysis method was validated by some case studies. We employed a thresholding scheme to discriminate between insufficient murmurs and control sounds. Three hundred 32 heart sounds with 126 control and 206 murmur cases were acquired from four healthy volunteers and 47 patients who suffered from heart defects. Control sounds were recorded by applying a wireless electric stethoscope system to subjects with no history of other heart complications. Insufficient murmurs were grouped into two valvular heart defect categories, aortic and mitral. These murmur subjects had no other coexistent valvular defects. The proposed insufficient murmur detection method yielded a high classification efficiency of 99.78% specificity and 99.43% sensitivity.