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The goal of our effort is to increase the accuracy of heart sound extraction from recordings that contain both heart and lung noises. We employ sophisticated signal processing methods, beginning with adaptive filtering using the Least Mean Squares (LMS) algorithm to separate cardiac sounds from the intricate background of cardiopulmonary noise. Subsequently, we utilize wavelet decomposition to eliminate residual noise from the recovered signals, guaranteeing the preservation of crucial cardiac data. The efficacy of our approach is confirmed by a comprehensive analysis employing metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Signal-To-Noise Ratio (SNR). This methodology exhibits significant promise for augmenting diagnostic precision in medical settings, offering a more expedient resolution for precisely assessing cardiac sounds.
Sathesh et al. (Fri,) studied this question.