An automatic approach for heart failure typing based on heart sounds and convolutional recurrent neural networks

作者全名："Wang, Hui; Guo, Xingming; Zheng, Yineng; Yang, Yang"

作者地址："[Wang, Hui; Guo, Xingming; Yang, Yang] Chongqing Univ, Coll Bioengn, Key Lab Biorheol Sci & Technol, Minist Educ, Chongqing 400044, Peoples R China; [Zheng, Yineng] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China"

通信作者："Guo, XM (通讯作者)，Chongqing Univ, Coll Bioengn, Key Lab Biorheol Sci & Technol, Minist Educ, Chongqing 400044, Peoples R China."

来源：PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE

ESI学科分类：　

WOS号：WOS:000778215500001

JCR分区：Q1

影响因子：4.4

年份：2022

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Convolutional neural network; Recurrent neural network; Minimal gated unit; Heart sounds; Heart failure typing

摘要："Heart failure (HF) is a complex clinical syndrome that poses a major hazard to human health. Patients with different types of HF have great differences in pathogenesis and treatment options. Therefore, HF typing is of great significance for timely treatment of patients. In this paper, we proposed an automatic approach for HF typing based on heart sounds (HS) and convolutional recurrent neural networks, which provides a new non-invasive and convenient way for HF typing. Firstly, the collected HS signals were preprocessed with adaptive wavelet denoising. Then, the logistic regression based hidden semi-Markov model was utilized to segment HS frames. For the distinction between normal subjects and the HF patients with preserved ejection fraction or reduced ejection fraction, a model based on convolutional neural network and recurrent neural network was built. The model can automatically learn the spatial and temporal characteristics of HS signals. The results show that the proposed model achieved a superior performance with an accuracy of 97.64%. This study suggests the proposed method could be a useful tool for HF recognition and as a supplement for HF typing."

基金机构："National Natural Science Foundation of China [31870980, 31800823, 31570003]"

基金资助正文："This study is funded by the National Natural Science Foundation of China (Nos. 31870980, 31800823, and 31570003)."