Electrocardiogram data recognition algorithm based on variable scale fusion network model_Journal of Biomedical Engineering

Authors：

LIU Zilong ,  CHEN Peng

School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;

Corresponding author：

CHEN Peng, Email: cp_usst@163.com

Keywords：

Arrhythmia; Variable scale fusion network; Electrocardiogram generative adversarial network; Unbalanced electrocardiogram data

DOI：

10.7507/1001-5515.202112045

Video：

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The judgment of the type of arrhythmia is the key to the prevention and diagnosis of early cardiovascular disease. Therefore, electrocardiogram (ECG) analysis has been widely used as an important basis for doctors to diagnose. However, due to the large differences in ECG signal morphology among different patients and the unbalanced distribution of categories, the existing automatic detection algorithms for arrhythmias have certain difficulties in the identification process. This paper designs a variable scale fusion network model for automatic recognition of heart rhythm types. In this study, a variable-scale fusion network model was proposed for automatic identification of heart rhythm types. The improved ECG generation network (EGAN) module was used to solve the imbalance of ECG data, and the ECG signal was reproduced in two dimensions in the form of gray recurrence plot (GRP) and spectrogram. Combined with the branching structure of the model, the automatic classification of variable-length heart beats was realized. The results of the study were verified by the Massachusetts institute of technology and Beth Israel hospital (MIT-BIH) arrhythmia database, which distinguished eight heart rhythm types. The average accuracy rate reached 99.36%, and the sensitivity and specificity were 96.11% and 99.84%, respectively. In conclusion, it is expected that this method can be used for clinical auxiliary diagnosis and smart wearable devices in the future.

Citation： LIU Zilong, CHEN Peng. Electrocardiogram data recognition algorithm based on variable scale fusion network model. Journal of Biomedical Engineering, 2022, 39(3): 570-578. doi: 10.7507/1001-5515.202112045 Copy

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23.	Zhang J, Tian J, Cao Y, et al. Deep time–frequency representation and progressive decision fusion for ECG classification. Knowledge-Based Systems, 2020, 190: 105402.
24.	Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: an overview. IEEE Signal Process Mag, 2018, 35(1): 53-65.
25.	魏国强, 周从华, 张婷. 基于多维分段和动态权重DTW的多元时间序列相似性度量方法. 计算机与数字工程, 2021, 49(11): 2299-2304, 2406.
26.	Wasimuddin M, Elleithy K, Abuzneid A, et al. ECG signal analysis using 2-D image classification with convolutional neural network//2019 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE, 2019: 949-954.
27.	Heravi E J, Aghdam H H, Puig D. An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods. Pattern Recognition Letters, 2018, 105: 50-58.
28.	Acharya U R, Oh S L, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Computers in biology and medicine, 2017, 89: 389-396.
29.	Oh S L, Ng E Y K, Tan R S, et al. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med, 2018, 102: 278-287.
30.	Zhou S, Tan B. Electrocardiogram soft computing using hybrid deep learning CNN-ELM. Applied Soft Computing, 2020, 86: 105778.
31.	Zhu Wenliang, Chen Xiaohe, Wang Yan, et al. Arrhythmia recognition and classification using ECG morphology and segment feature analysis. IEEE/ACM Trans Comput Biol Bioinform, 2019, 16(1): 131-138.

1. Ebrahimi Z, Loni M, Daneshtalab M, et al. A review on deep learning methods for ECG arrhythmia classification. Expert Systems with Applications: X, 2020: 100033.
2. Anumonwo J B, Kalifa J. Risk factors and genetics of atrial fibrillation. Heart Fail Clin, 2016, 12(2): 157-166.
3. Chen A, Wang F, Liu W, et al. Multi-information fusion neural networks for arrhythmia automatic detection. Comput Methods Programs Biomed, 2020, 193: 105479.
4. Zeng Nianyin, Wang Zidong, Zhang Hong. Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter. Science China Information Sciences, 2016, 59(11): 112204.
5. Raj S, Ray K C. ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Trans Instrum Meas, 2017, 66(3): 470-478.
6. Shi H, Wang H, Huang Y, et al. A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Comput Methods Programs Biomed, 2019, 171: 1-10.
7. Shyu L Y, Wu Y H, Hu W. Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG. IEEE Trans Biomed Eng, 2004, 51(7): 1269-1273.
8. Martis R J, Acharya U R, Min L C. ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Process Control, 2013, 8(5): 437-448.
9. Xu S S, Mak M W, Cheung C C. Towards end-to-end ECG classification with raw signal extraction and deep neural networks. IEEE J Biomed Health Inform, 2019, 23(4): 1574-1584.
10. Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng, 2016, 63(3): 664-675.
11. Rohmantri R, Surantha N. Arrhythmia classification using 2D convolutional neural network. Int. J. Adv. Comput. Sci. Appl, 2020, 11: 201-208.
12. 李端, 张洪欣, 刘知青, 等. 基于深度残差卷积神经网络的心电信号心律不齐识别. 生物医学工程学杂志, 2019, 36(2): 189-198.
13. 王自强, 刘洪运, 石金龙, 等. 基于卷积神经网络的心电图心博识别. 中国医学物理学杂志, 2019, 36(8): 938-944.
14. Hammad M, Zhang S, Wang K. A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Future Generation Computer Systems, 2019, 101: 180-196.
15. LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 1995, 3361(10): 1995.
16. Moody G B, Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(3): 45-50.
17. Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
18. 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.
19. Han Chuanqi, Yu Fang, Wang Peng, et al. Length no longer matters: a real length adaptive arrhythmia classification model with multi-scale convolution//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2021: 1295-1299.
20. Mathunjwa B M, Lin Y T, Lin C H, et al. ECG arrhythmia classification by using a recurrence plot and convolutional neural network. Biomedical Signal Processing and Control, 2021, 64: 102262.
21. Zeng M, Zhang X, Zhao C, et al. GRP-DNet: a gray recurrence plot-based densely connected convolutional network for classification of epileptiform EEG. Journal of Neuroscience Methods, 2021, 347: 108953.
22. 谢佳玲, 龚渝顺, 魏良, 等. 一种针对可穿戴设备的抗运动干扰心率检测算法. 生物医学工程学杂志, 2021, 38(4): 764-773.
23. Zhang J, Tian J, Cao Y, et al. Deep time–frequency representation and progressive decision fusion for ECG classification. Knowledge-Based Systems, 2020, 190: 105402.
24. Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: an overview. IEEE Signal Process Mag, 2018, 35(1): 53-65.
25. 魏国强, 周从华, 张婷. 基于多维分段和动态权重DTW的多元时间序列相似性度量方法. 计算机与数字工程, 2021, 49(11): 2299-2304, 2406.
26. Wasimuddin M, Elleithy K, Abuzneid A, et al. ECG signal analysis using 2-D image classification with convolutional neural network//2019 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE, 2019: 949-954.
27. Heravi E J, Aghdam H H, Puig D. An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods. Pattern Recognition Letters, 2018, 105: 50-58.
28. Acharya U R, Oh S L, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Computers in biology and medicine, 2017, 89: 389-396.
29. Oh S L, Ng E Y K, Tan R S, et al. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med, 2018, 102: 278-287.
30. Zhou S, Tan B. Electrocardiogram soft computing using hybrid deep learning CNN-ELM. Applied Soft Computing, 2020, 86: 105778.
31. Zhu Wenliang, Chen Xiaohe, Wang Yan, et al. Arrhythmia recognition and classification using ECG morphology and segment feature analysis. IEEE/ACM Trans Comput Biol Bioinform, 2019, 16(1): 131-138.

Journal of Biomedical Engineering

Electrocardiogram data recognition algorithm based on variable scale fusion network model

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