Wavelet based segmentation and time-frequency caracterisation of some abnormal heart sound signals

Biomedical signal recordings are so complex and nonstationnary, they are also affected by different kinds of noise that make their interpretation difficult. The major goal of the paper consists of two ideas. In the first one, we present the results of segmentation method followed by the time frequency caracterisation of some phonocardiogram (PCG) signals. The paper using the Discrete Wavelet Transform (DWT) in conjunction with Shannon entropy provided a useful tool for phonocardiogram (PCG) segmentation. In the segmentation technique, we calculate the entropy of the details coefficients at each level and threshold it in order to detect the murmur of heart sound signals. Several real-life signals were used: Early Aortic Stenosis (EAS), Late Aortic stenosis (LAS), Mitral Regurgitation (MR), Aortic Regurgitation (AR) .The results of the method illustrate clearly the detection of the main components S1, S2, S3, pathological murmurs and the identification of the valves disease.