Heart sound classification with signal instant energy and stacked autoencoder network

Abstract Recently, different signal processing and classification methods have been tried to increase the success of classification for a heart sound analysis. For this purpose, in many studies, S1 and S2 segments of heart sounds were obtained by using methods such as Shannon energy, discrete time wavelet transform, Hilbert transform, and then classified. In this study, the use of signal energy, which is generally used to segment S1-S2 sounds in heart sounds, in direct classification was investigated. The instant energies of the heart sounds obtained by the resampled energy method were used directly for classification. The classification was done with a stacked autoencoder network. Experiments were carried out with the PASCAL B-training data set to test the performance of the proposed method. The results were compared with the data from previous studies for the same data set. As a result of the research, it is seen that the classification performance criterias obtained with the proposed method are as similar as the segmented classification. Thus, it was concluded that the instant energy of the heart sounds, and a stacked autoencoder networks can be very easily used for the diagnosis of heart diseases from heart sounds and a more efficient, and effective classification performance can be obtained.