Denoising of Heart Sound Signals Using Discrete Wavelet Transform

Signal denoising remains to be one of the main problems in the field of signal processing. Various signal denoising algorithms using wavelet transforms have been introduced. Wavelets show superior signal denoising performance due to their properties such as multiresolution and windowing. This study focuses on denoising of phonocardiogram (PCG) signals using different families of discrete wavelet transforms, thresholding types and techniques, and signal decomposition levels. In particular, we discuss the effect of the chosen wavelet function and wavelet decomposition level on the efficiency of the denoising algorithm. Denoised signals are compared with the original PCG signal to determine the most suitable parameters (wavelet family, level of decomposition, and thresholding type) for the denoising process. The performance of our algorithm is evaluated using the signal-to-noise ratio, percentage root-mean-square difference, and root-mean-square error. The results show that the level of decomposition and thresholding type are the most important parameters affecting the efficiency of the denoising algorithm. Finally, we compare our results with those from other studies to test and optimize the performance of the proposed algorithm.