Heart sound classification using wavelet transform and incremental self-organizing map

Abstract

Determination of heart condition by heart auscultation is a difficult task and requires special training of medical staff. Computerized techniques suggest objective and more accurate results in a fast and easy manner. Hence, in this study it is aimed to perform computer-aided heart sound analysis to give support to medical doctors in decision making. In this study, a novel method is presented for the classification of heart sounds (HSs). Discrete wavelet transform is applied to windowed one cycle of HS. Wavelet transform is used both for the segmentation of S1–S2 sounds and determination of the features. Based on the third, fourth and the fifth decomposition-level detail coefficients, the timings of S1–S2 sounds are determined by an adaptive peak-detector. For the feature extraction, powers of detail coefficients in all five sub-bands are utilized. In the classification stage, Kohonen's SOM network and an incremental self-organizing map (ISOM) are examined comparatively. In order to increase the performance of heart sound classification, an incremental neural network is proposed in this study. It is observed that ISOM successfully classifies the HSs even in noisy environment.