Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism_Journal of Biomedical Engineering

Authors：

YUAN Chengcheng ¹ , LIU Zijie ^2,3 , WANG Changqing ¹ ,  YANG Fei ¹

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;
3. University of Science and Technology of China, Hefei 230026, P. R. China;

Corresponding author：

YANG Fei, Email: yangfei@ahmu.edu.cn

Keywords：

Electrocardiogram signals classification; Residual network; Bi-directional gated recurrent unit; Self-attention mechanism

DOI：

10.7507/1001-5515.202210062

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： YUAN Chengcheng, LIU Zijie, WANG Changqing, YANG Fei. Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism. Journal of Biomedical Engineering, 2023, 40(3): 474-481. doi: 10.7507/1001-5515.202210062 Copy

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15.	Kumar A,Tomar H,Mehla V K,et al. Stationary wavelet transform based ECG signal denoising method. ISA Trans, 2021, 114: 251-262.
16.	Han G,Lin B,Xu Z. Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview. J Instrum, 2017, 12(3): 3010-3029.
17.	Singh P, Pradhan G. Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering. Australas Phys Eng Sci Med, 2018, 41(4): 891-904.
18.	Moody G B,Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag, 2001, 20(3): 45-50.
19.	Hulse J V, Khoshgoftaar T M, Napolitano A. An empirical evaluation of repetitive undersampling techniques. Int J Softw Eng Knowl Eng, 2012, 20(2): 173-195.
20.	Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 2002: 321-357.
21.	He Haibo, Bai Yang, Garcia E A, et al. ADASYN: adaptive synthetic sampling approach for imbalanced learning//2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), HongKong: IEEE, 2008: 1322-1328.
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23.	Jhang Y S, Wang S T, Sheu M H, et al. Integration design of portable ECG signal acquisition with deep-learning based electrode motion artifact removal on an embedded system. IEEE Access, 2022, 10: 57555-57564.

1. 胡盛寿. 中国心血管健康与疾病报告2021概要. 中国循环杂志, 2022, 37(6): 553-578.
2. Wang J S,Chiang W C,Hsu Y L,et al. ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing, 2013, 116(10): 38-45.
3. Li T Y,Zhou M. ECG classification using wavelet packet entropy and random forests. Entropy, 2016, 18(8): 285-294.
4. Pandey S K,Janghel R R,Vani V. Patient specific machine learning models for ECG signal classification. Procedia Computer Science, 2020, 167: 2181-2190.
5. Sellami A,Hwang H. A robust deep convolutional neural network with batch weighted loss for heartbeat classification. Expert Syst Appl, 2019, 122: 75-84.
6. Chen C,Hua Z,Zhang R,et al. Automated arrhythmia classification based on a combination network of CNN and LSTM. Biomed Signal Proces Control, 2020, 57: 101819.
7. Gao Yibo,Wang Huan,Liu Zuhao. A novel approach for atrial fibrillation signal identification based on temporal attention mechanism// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC). IEEE,2020: 316-319.
8. Yao G,Mao X,Li N,et al. Interpretation of electrocardiogram heartbeat by CNN and GRU. Comput Math Method Med, 2021, 2021: 6534942.
9. Zhou S R,Tan B. Electrocardiogram soft computing using hybrid deep learning CNN-ELM. Appl Soft Comput, 2019, 86: 105778.
10. Li Z,Zhou D,Wan L,et al. Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram. J Electrocardiol, 2020, 58: 105-112.
11. Zhou Z, Zhai X, Tin C. Fully automatic electrocardiogram classification system based on generative adversarial network with auxiliary classifier. Expert Syst Appl, 2021, 174: 114809.
12. Che C, Zhang P, Zhu M, et al. Constrained transformer network for ECG signal processing and arrhythmia classification. BMC Med Inform Decis Mak, 2021, 21(1): 184.
13. Vaswani A,Shazeer N,Parmar N,et al. Attention is all you need//31st Conference on Neural Information Processing Systems, New York: Curran Associates, 2017, 55: 5998-6008.
14. Wang H,Shi H,Lin K,et al. A high-precision arrhythmia classification method based on dual fully connected neural network. Biomed Signal Process Control, 2020, 58: 101874.
15. Kumar A,Tomar H,Mehla V K,et al. Stationary wavelet transform based ECG signal denoising method. ISA Trans, 2021, 114: 251-262.
16. Han G,Lin B,Xu Z. Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview. J Instrum, 2017, 12(3): 3010-3029.
17. Singh P, Pradhan G. Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering. Australas Phys Eng Sci Med, 2018, 41(4): 891-904.
18. Moody G B,Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag, 2001, 20(3): 45-50.
19. Hulse J V, Khoshgoftaar T M, Napolitano A. An empirical evaluation of repetitive undersampling techniques. Int J Softw Eng Knowl Eng, 2012, 20(2): 173-195.
20. Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 2002: 321-357.
21. He Haibo, Bai Yang, Garcia E A, et al. ADASYN: adaptive synthetic sampling approach for imbalanced learning//2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), HongKong: IEEE, 2008: 1322-1328.
22. Oksuz K, Cam B C, Kalkan S, et al. Imbalance problems in object detection: a review. IEEE Trans Pattern Anal Mach Intell, 2021, 43(10): 3388-3415.
23. Jhang Y S, Wang S T, Sheu M H, et al. Integration design of portable ECG signal acquisition with deep-learning based electrode motion artifact removal on an embedded system. IEEE Access, 2022, 10: 57555-57564.

Journal of Biomedical Engineering

Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism

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