Classification of PCG Signals: A Survey

Abstract

Heart sounds are multi component non-stationary signals characterized as the normal phonocardiogram (PCG) signals and the pathological PCG signals. PCG is a weak biological signal mixed with strong background noise susceptible to interference from noise. The noise may be added due to various sources. The PCG signal has specific individual characteristics which are considered as a physiological sign in a biometric system. Literatures suggest that the method on time-frequency analysis is known as the trimmed mean spectrogram (TMS). The abnormal murmurs in heart sound can be diagnosed. Another method in time-frequency domain is used in which features are extracted from the TMS containing the distribution of the systolic and diastolic signatures. Probability Neural Networks (PNNs) are used in feature extraction with the acoustic intensities in systole and diastole. These methods can detect accurately the heart disease depending on the applied PCG signal but the result obtained is not optimum. An adaptive neuro-fuzzy inference system (ANFIS) is suggested that can correctly detect the pathological condition of heart.