The analysis of the pathological linear association degree of a phonocardiogram (PCG) signals by applying coherences.

Abstract

The recorded phonocardiogram PCG signal is often contaminated by different types of noises that can be seen in the frequency band of PCG signal, which may change the charac-teristics of this signal. Discrete wavelet transform ( DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signals denoising. Our ap-proach consists of two algorithms are designed to tackle the problems of noise and variabil-ity caused by PCG acquisition in a real clinical environment for different categories of pa-tients whereas the obtained results are evaluated by examining coherence analysis, correla-tion coefficient, and in term of mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.