Heart Sound Segmentation Based on a Joint HSMM Method

In this study, we propose a novel Joint-HSMM method that combines the CNN and probabilistic models (HSMM) for heart sound segmentation. First, we build a semantic segmentation model and a time series probability model based on U-Net and HSMM respectively, and fusion model results by average consistency. The proposed method are conducted on various datasets, including the publicly available PhysioNet and a children heart sound dataset collected by Shanghai Xinhua Hospital. For the second dataset, we make use of Transfer Learning techniques for early convergence and robustness. It is shown that our method has outperformed the current state-of-the-art method by achieving the average recall, F1-score of 96.29%, 96.14% on PhysioNet dataset, and 86.95% and 87.24% on children heart sound dataset respectively.