论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
基于 HSMM 和深度神经网络的心音异常识别系统设计
Authors Yin H, Ma Q, Zhuang J, Yu W, Wang Z
Received 30 March 2022
Accepted for publication 15 August 2022
Published 19 August 2022 Volume 2022:15 Pages 285—292
DOI https://doi.org/10.2147/MDER.S368726
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Scott Fraser
Introduction: Heart sound signal is an important physiological signal of human body, and the identification and research of heart sound signal is of great significance.
Methods: For abnormal heart sound signal recognition, an abnormal heart sound recognition system, combining hidden semi-Markov models (HSMM) with deep neural networks, is proposed. Firstly, HSMM is used to build a heart sound segmentation model to accurately segment the heart sound signal, and then the segmented heart sound signal is subjected to feature extraction. Finally, the trained deep neural network model is used for recognition.
Results: Compared with other methods, this method has a relatively small amount of input feature data and high accuracy, fast recognition speed.
Discussion: HSMM combined with deep neural network is expected to be deployed on smart mobile devices for telemedicine detection.
Keywords: heart sound signal, recognition, hidden semi-Markov, neural network