A multi-channel UNet framework based on SNMF-DCNN for robust heart-lung-sound separation

Cardiopulmonary and cardiovascular diseases are fatal factors that threaten human health and cause many deaths worldwide each year, so it is essential to screen cardiopulmonary disease more accurately and efficiently. Auscultation is a non-invasive method for physicians' perception of the disease. The Heart Sounds (HS) and Lung Sounds (LS) recorded by an electronic stethoscope consist of acoustic information that is helpful in the diagnosis of pulmonary conditions. Still, inter-interference between HS and LS presented in both the time and frequency domains blocks diagnostic efficiency. This paper proposes a blind source separation (BSS)strategy that first classifies Heart-Lung-Sound (HLS) according to its LS features and then separates it into HS and LS. Sparse Non-negative Matrix Factorization (SNMF) is employed to extract the LS features in HLS, then proposed a network constructed by Dilated Convolutional Neural Network (DCNN) to classify HLS into five types by the magnitude features of LS. Finally, Multi-Channel UNet (MCUNet) separation model is utilized for each category of HLS. This paper is the first to propose the HLS classification method SNMF-DCNN and apply UNet to the cardiopulmonary sound separation domain. Compared with other state-of-the-art methods, the proposed framework in this paper has higher separation quality and robustness.