A framework of heart sound noise reduction using multi-channel structure - Guo, BinBin; Tang, Hong (Biomedical signal processing and control, 2024)
•We proposed a novel method for noise reduction of multichannel heart sound signals, which was based on FastICA algorithm and CNN model.•The method performs classification and consistency analysis of the independent components to extract reliable independent components of heart sounds.•The method improves the SNRs of each channel signal up to 20 dB, which is superior to the Wiener filtering and singular spectrum analysis (SSA). Various noises could contaminate heart sounds and yield inaccurate diagnosis. This paper proposed a framework of heart sound noise reduction using multi-channel structure. Multi-channel heart sound (MCHS) signals were preprocessed, and then decomposed into independent components (ICs) using FastICA algorithm. The ICs were evaluated for consistency using correlation analysis, and reliable ICs were identified. The reliable ICs were classified based on a convolutional neural network (CNN) into two groups: one is of ICs for heart sounds and the other is of ICs for noise, respectively. Consequently, the MCHS signals were reconstructed from the ICs group of heart sounds. To validate the proposed framework, the MCHS signals of 15 subjects were acquired and mixed with breath sounds under different signal-to-noise ratios (SNRs). the simulation results showed that the framework had effective noise reduction performances in suppressing the breath sounds. The SNR of the denoised MCHS could be improved to 20 dB. the proposed framework was better than traditional methods, such as Wiener filter and singular spectrum analysis (SSA). The practical applications proved that the framework is possible to reconstruct the heart sound signals in low signal quality situation.