Abstract
A novel method is presented to determine the features of heart sounds. Feature vectors are formed by using the wavelet detail and approximation coefficients at the second and the sixth decomposition levels, respectively. Decision making is performed in four stages: Segmentation of the first and second heart sounds, normalization process, feature extraction, and classification by the artificial neural network. In this study, nine different heart sounds are classified.
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