Classification of heart sounds using an artificial neural network

A novel method is presented for the classification of heart sounds (HSs). Wavelet transform is applied to a window of two periods of HSs. Two analyses are realized for the signals in the window: segmentation of the first and second HSs, and extraction of the features. After the segmentation, feature vectors are formed by using the wavelet detail coefficients at the sixth decomposition level. The best feature elements are analyzed by using dynamic programming. Grow and learn (GAL) network and linear vector quantization (LVQ) network are used for the classification of seven different HSs. It is observed that HSs of patients are successfully classified by the GAL network compared to the LVQ network.