Develop the Hybrid Empirical Mode Decomposition with Fast Mask CNN to Improve the Performance Measures of PCG Signals

The Phonocardiogram (PCG) signal provides crucial insights into heart function and is instrumental in identifying cardiac dysfunctions leading to heart failure.Given the significant impact of Cardiovascular Diseases (CVD) on human life and socioeconomic conditions, early detection of cardiac problems is imperative.This study evaluates a hybrid denoising technique, Empirical Mode Decomposition (EMD) with Fast Mask Convolutional Neural Network (EMD-FMCNN), to reduce the impact of noise on PCG signals.Using datasets from the NHS supplemented with Additive White Gaussian Noise (AWGN), the effectiveness of the EMD-FMCNN approach is compared to the Double-Density Discrete Wavelet Transform (DD-DWT) methodology.Evaluation metrics such as Mean Square Error (MSE) and Signal-to-Noise Ratio (SNR) are utilized.The results indicate that the EMD-FMCNN approach outperforms DD-DWT, yielding superior SNR values of 25.55 dB compared to 18.19 dB, and achieving optimal MSE values of 0.01% compared to 0.42% for DD-DWT.The findings demonstrate the effectiveness of the EMD-FMCNN approach in denoising PCG signals, offering a promising method for improving the accuracy of cardiovascular disease diagnosis.