Heart Sound Classification based on Discrete Wavelet Transform and Group-based Sparse Features of PCG Signal

Heart sounds play an important role in diagnosing heart diseases. The phonocardiogram (PCG), is the recording of the sounds produced by the heart and the main heart valves. The PCG signal can be recorded invasively by internal microphones that are inserted into the heart and valves as well as noninvasively by placing the microphone on the surface of the body. A main challenge of PCG signal is environmental factors which reduces the signal-to-noise ratio (SNR). This has made it difficult and time-consuming for experts to detect heart sounds. Therefore, automatic classification of heart sounds is very helpful for physicians. In this paper, the features are extracted by the Discrete Wavelet Transform (DWT) and group-based sparse to distinguish the main and abnormal heart sounds. Afterwards, the Support Vector Machine (SVM) algorithm is used to classify these heart sounds. The simulation results on the PhysioNet dataset affirm the suggested method outperforms classical methods in terms of sensitivity, precision, F1-score and, specificity.