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

• Published in: Australasian physical & engineering sciences in medicine Vol. 46; no. 1; pp. 279 - 288
• Main Authors: Chen, Xiao, Guo, Xingming, Zheng, Yineng, Lv, Chengcong
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2023; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial neural networks; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Cardiac function; Classification; Continuous wavelet transform; Feature extraction; Heart; Heart function; Heart Sounds; Humans; Markov chains; Medical and Radiation Physics; Neural networks; Neural Networks, Computer; Noise reduction; Regression models; Scientific Paper; Wavelet Analysis; Wavelet transforms; Heart sounds; NYHA classification; Cardiac function classification; Convolutional neural network
• ISSN: 2662-4729; 0158-9938; 2662-4737; 2662-4737; 1879-5447
• DOI: 10.1007/s13246-023-01216-9

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.