Heart function grading evaluation based on heart sounds and convolutional neural networks

作者全名："Chen, Xiao; Guo, Xingming; Zheng, Yineng; Lv, Chengcong"

作者地址："[Chen, Xiao; Guo, Xingming; Lv, Chengcong] Chongqing Univ, Coll Bioengn, Key Lab Biorheol Sci & Technol, Minist Educ, Chongqing 400044, Peoples R China; [Zheng, Yineng] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 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:000920199700001

JCR分区：Q2

影响因子：2.4

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Heart sounds; Convolutional neural network; Cardiac function classification; NYHA classification

摘要："Accurate and rapid cardiac function assessment is critical for disease diagnosis and treatment strategy. However, the current cardiac function assessment methods have their adaptability and limitations. Heart sounds (HS) can reflect changes in heart function. Therefore, HS signals were proposed to assess cardiac function, and a specially designed pruning convolutional neural network (CNN) was applied to recognize subjects' cardiac function at different levels in this paper. Firstly, the adaptive wavelet denoising algorithm and logistic regression based hidden semi-Markov model were utilized for signal denoising and segmentation. Then, the continuous wavelet transform (CWT) was employed to convert the preprocessed HS signals into spectra as input to the convolutional neural network, which can extract features automatically. Finally, the proposed method was compared with AlexNet, Resnet50, Xception, GhostNet and EfficientNet to verify the superiority of the proposed method. Through comprehensive comparison, the proposed approach achieves the best classification performance with an accuracy of 94.34%. The study indicates HS analysis is a non-invasive and effective method for cardiac function classification, which has broad research prospects."

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

基金资助正文：This study was supported by the National Natural Science Foundation of China (No. 31870980 and No. 31800823).