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A novel heart sound segmentation algorithm via multi-feature input and neural network with attention mechanism

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机构： [1]School of Information Science and Technology, Yunnan University, Kunming 650504, People's Republic of China. [2]Yunnan Fuwai Cardiovascular Disease Hospital, Kunming 650102, People's Republic of China.

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关键词： heart sound segmentation intrinsic mode function instantaneous phase waveform neural network attention mechanism

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Objective. Heart sound segmentation (HSS), which aims to identify the exact positions of the first heart sound(S1), second heart sound(S2), the duration of S1, systole, S2, and diastole within a cardiac cycle of phonocardiogram (PCG), is an indispensable step to find out heart health. Recently, some neural network-based methods for heart sound segmentation have shown good performance.Approach. In this paper, a novel method was proposed for HSS exactly using One-Dimensional Convolution and Bidirectional Long-Short Term Memory neural network with Attention mechanism (C-LSTM-A) by incorporating the 0.5-order smooth Shannon entropy envelope and its instantaneous phase waveform (IPW), and third intrinsic mode function (IMF-3) of PCG signal to reduce the difficulty of neural network learning features.Main results. An average F1-score of 96.85 was achieved in the clinical research dataset (Fuwai Yunnan Cardiovascular Hospital heart sound dataset) and an average F1-score of 95.68 was achieved in 2016 PhysioNet/CinC Challenge dataset using the novel method.Significance. The experimental results show that this method has advantages for normal PCG signals and common pathological PCG signals, and the segmented fundamental heart sound(S1, S2), systole, and diastole signal components are beneficial to the study of subsequent heart sound classification.© 2022 IOP Publishing Ltd.

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第一作者机构： [1]School of Information Science and Technology, Yunnan University, Kunming 650504, People's Republic of China.

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