Denoising of PCG Signals: A Comparative Study of DWT and Adaptive based Algorithms

Phonocardiography is a widely used procedure for understanding the functioning of human heart and diagnosing heart diseases. However, the Phonocardiogram (PCG) signals obtained during this process are susceptible to noise, so its imperative to eliminate such noise to ensure accurate heart disease diagnosis. Various algorithms are available for denoising biomedical signals, and this study aims to compare the effective- ness of two distinct algorithmic approaches: Discrete Wavelet Transform (DWT) based methods and adaptive-based methods for denoising PCG signals. The DWT-based approach involves breaking down the PCG signal into different frequency sub- bands using filtering and down-sampling techniques, enabling noise reduction within specific frequency ranges. On the other hand, adaptive-based algorithms employ techniques such as Least Mean Square (LMS) and Recursive Least Squares (RLS) to estimate and remove noise from the original signal. To assess the performance of these denoising methods, various performance metrics are utilized, including Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE), and Percentage Root Mean Square Difference (PRD). The results of this study indicate that different methods of DWT and adaptive-based algorithms effectively reduce noise in PCG signals. However, DWT exhibits superior performance in high-noise environments, while adaptive- based algorithms excel when dealing with signals characterized by higher noise power. These findings emphasize the importance of selecting a denoising algorithm based on the specific noise characteristics of the analysis environment and application.