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Analysis of PCG signals helps in diagnosis of cardio vascular disorder non-invasively. PCG signals are non-stationery in nature and hence time-frequency analysis of PCG is the most suitable means for analysis to determine the basic features of it. However, the PCG signals need to be denoised before feature extraction process and DWT proves to be most suitable for this purpose. During acquisition of HSS technically known as PCG various types of noises and artifacts contaminate the signal of interest. Hence denoising of the signal is inevitable before proceeding for diagnosis. DWT has been proved to be a powerful and handy tool along with thresholding for this purpose. However, the main challenge lies in the fact of selection of the suitable MWT with required number of DL and the type of thresholding function. The present work deals with the optimization of the selection process using varieties of MWT with varying DL and thresholding functions. Rigorous experiments have been conducted using codes in MATLAB environment to select the suitable MWT, DL and thresholding function. After optimization, the selected MWT, DL and Thresholding function have been applied on 22 PCG signals obtained from open data source and the performance of the process has been measured in terms of SNR and RMSE. It has been observed from the extensive experiments using different combination that sym20 wavelet with 10 decomposition level along with Bayesian Soft thresholding function provide the best result in denoising the applied PCG signals. The database used is that of MHSDB available at www.med.umich.edu/Irc/psb/heartsounds/index.htm provided by the University of Michigan Health System.
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