Detection of valvular heart disorders using wavelet packet decomposition and support vector machine

In this study, the valvular heart disorder (VHD) detection method by the wavelet packet (WP) decomposition and the support vector machine (SVM) techniques are proposed. From considering the truth that the frequency ranges of the normal sound and VHDs are different from each other, the WP decomposition at level 8 is employed to split more elaborate frequency bandwidths of the heart sound signals. And then the WP energy (WPE) with the distribution information of energy throughout the whole frequency range of heart sound signals is calculated. Since the heart sound signals with the frequency range of 20–750 Hz are preferred in this study, WPEs at the terminal nodes from (8, 1) to (8, 47) are selected and two parameters meanWPE and stdWPE as defined by the mean value and standard deviation of the position indices of the terminal nodes with over the weighting value (ζ) of the maximum value of WPE are proposed as a feature. Furthermore, the SVM technique is employed as the identification tool to classify between the normal sound and VHDs. Finally, a case study on the normal sound, aortic and mitral VHDs is demonstrated to validate the usefulness and efficiency of the VHD detection using WP decomposition and SVM classifier. The experimental results of the proposed VHD detection method showed high performance like the specificity of over 96% and the sensitivity of 100% for both the training and testing data.