Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism.
10.7507/1001-5515.202210062
- Author:
Chengcheng YUAN
1
;
Zijie LIU
2
;
Changqing WANG
1
;
Fei YANG
1
Author Information
1. School of Biomedical Engineering, Anhui Medical University, Hefei 230009, P. R. China.
2. Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, P. R. China.
- Publication Type:Journal Article
- Keywords:
Bi-directional gated recurrent unit;
Electrocardiogram signals classification;
Residual network;
Self-attention mechanism
- MeSH:
Humans;
Electrocardiography;
Algorithms;
Cardiovascular Diseases;
Databases, Factual;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2023;40(3):474-481
- CountryChina
- Language:Chinese
-
Abstract:
In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.
