Arrhythmia classification method based on ResDCGAN and improved residual network
10.3969/j.issn.1005-202X.2025.10.017
- VernacularTitle:基于ResDCGAN和改进残差网络的心律失常分类方法
- Author:
Peiling ZHANG
1
;
Shuo ZHANG
1
Author Information
1. 河南理工大学物理与电子信息学院,河南 焦作 454003
- Publication Type:Journal Article
- Keywords:
arrhythmia classification;
data balancing;
ResDCGAN;
coordinate attention;
ResNet34
- From:
Chinese Journal of Medical Physics
2025;42(10):1384-1392
- CountryChina
- Language:Chinese
-
Abstract:
Arrhythmia is a common cardiovascular disease,and its diagnosis primarily relies on electrocardiograms(ECG).Leveraging computational techniques to achieve automatic arrhythmia classification can avoid human errors while improving diagnostic efficiency.An arrhythmia classification method based on a residual-structured deep convolutional generative adversarial network(ResDCGAN)and an improved ResNet34 is proposed to address the class imbalance in ECG data and the limitation of one-dimensional ECG processing.Specifically,the one-dimensional ECG signals are firstly denoised using variational mode decomposition.These denoised signals are then converted into two-dimensional Gramian angular summation field images.Subsequently,the proposed ResDCGAN is employed for data balancing,and finally,arrhythmia classification is carried out using a ResNet34 enhanced with coordinate attention.Experimental tests on the MIT-BIH arrhythmia database show that the proposed method achieves improvements of 0.22%,1.60%,1.89%,and 1.73%in accuracy,precision,recall rate,and F1-score,respectively,obtaining an accuracy of 99.66%.These results fully demonstrate the effectiveness of the proposed method,providing an effective solution for ECG data balancing and arrhythmia classification.
