Phonocardiogram signals (PCGs) represent a nonstationary signal due to their complicated production. Also, during the registration they may be added with different noise and pathological murmurs. Indeed, in real situation, the heart sound signal (HSs) may present some abnormal murmur characterizing a variety of heart diseases. This work deals with the segmentation of pathological PCGs based on the Discrete Wavelet Transform (DWT) which permits signal decomposition in different frequency bands. After the decomposition step, we estimate the Renyi Entropy (RE) of the detail coefficients. Then, we apply a threshold allowing detecting the murmur of the PCGs. After the detection, we characterize the results in time–frequency domain in order to extract some features such as frequency band, peak frequency and time duration of the abnormal murmur. The validation of the method is evaluated and proved using some pathological PCGs such as: Early Aortic Stenosis (EAS), Late Aortic Stenosis (LAS), Mitral Regurgitation (MR), Aortic Regurgitation (AR), Opening Snap (OS) and Pulmonary Stenosis (PS). The method presents good results in terms of the detection and the characterization of the main components and the abnormal murmurs associated with some valves disease.