Electrocardiogram classification algorithm based on CvT-13 and multimodal image fusion_Journal of Biomedical Engineering

Authors：

 LI Guoquan ¹ , ZHU Shuangqing ¹ , LIU Zitong ¹ , LIN Jinzhao ^1,2 , PANG Yu ²

1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China;
2. Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmission Technology, Chongqing 400065, P. R. China;

Corresponding author：

LI Guoquan, Email: ligq@cqupt.edu.cn

Keywords：

Electrocardiography; Classification of arrhythmias; Myocardial infarction; Multimodal image fusion; CvT-13

DOI：

10.7507/1001-5515.202301026

Video：

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Electrocardiogram (ECG) signal is an important basis for the diagnosis of arrhythmia and myocardial infarction. In order to further improve the classification effect of arrhythmia and myocardial infarction, an ECG classification algorithm based on Convolutional vision Transformer (CvT) and multimodal image fusion was proposed. Through Gramian summation angular field (GASF), Gramian difference angular field (GADF) and recurrence plot (RP), the one-dimensional ECG signal was converted into three different modes of two-dimensional images, and fused into a multimodal fusion image containing more features. The CvT-13 model could take into account local and global information when processing the fused image, thus effectively improving the classification performance. On the MIT-BIH arrhythmia dataset and the PTB myocardial infarction dataset, the algorithm achieved a combined accuracy of 99.9% for the classification of five arrhythmias and 99.8% for the classification of myocardial infarction. The experiments show that the high-precision computer-assisted intelligent classification method is superior and can effectively improve the diagnostic efficiency of arrhythmia as well as myocardial infarction and other cardiac diseases.

Citation： LI Guoquan, ZHU Shuangqing, LIU Zitong, LIN Jinzhao, PANG Yu. Electrocardiogram classification algorithm based on CvT-13 and multimodal image fusion. Journal of Biomedical Engineering, 2023, 40(4): 736-742. doi: 10.7507/1001-5515.202301026 Copy

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11.	刘晋瑞, 宋婷, 舒智林, 等. 一种面向运动解码的EEG-fNIRS时频特征融合与协同分类方法. 仪器仪表学报, 2022, 43(7): 165-173.
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19.	古莹奎, 吴宽, 李成. 基于格拉姆角场和迁移深度残差神经网络的滚动轴承故障诊断. 振动与冲击, 2022, 41(21): 228-237.
20.	张二华, 单德山, 李乔. 基于多尺度递归图理论的桥梁微弱信号非线性非平稳检验. 振动与冲击, 2019, 38(16): 123-128.
21.	何浩祥, 王玮, 黄磊. 基于卷积神经网络和递归图的桥梁损伤智能识别. 应用基础与工程科学学报, 2020, 28(4): 966-980.
22.	柯丽, 王丹妮, 杜强, 等. 基于卷积长短时记忆网络的心律失常分类方法. 电子与信息学报, 2020, 42(8): 1990-1998.
23.	Acharya U R, Fujita H, Oh S L, et al. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inform Sciences, 2017, 415: 190-198.
24.	Li W, Tang Y M, Yu K M, et al. SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis. Inform Sciences, 2022, 589: 738-750.

1. 刘子龙, 陈鹏. 基于变尺度融合网络模型的心电数据识别算法. 生物医学工程学杂志, 2022, 39(3): 570-578.
2. Le M D, Rathour V S, Truong Q S, et al. Multi-module recurrent convolutional neural network with transformer encoder for ECG arrhythmia classification// 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Athens: IEEE, 2021: 1-5.
3. 吴燃, 唐清垚, 姜小明, 等. 心电成分的多核尺度卷积神经网络分类算法研究. 重庆邮电大学学报, 2022, 34(2): 243-249.
4. 许诗雨, 莫思特, 闫惠君, 等. 基于焦点损失函数的嵌套长短时记忆网络心电信号分类研究. 生物医学工程学杂志, 2022, 39(2): 301-310.
5. 杨淑莹, 桂彬彬, 陈胜勇. 基于小波分解和1D-GoogLeNet的心律失常检测. 电子与信息学报, 2021, 43(10): 3018-3027.
6. 彭向东, 潘从成, 柯泽浚, 等. 基于并行架构和时空注意力机制的心电分类方法. 浙江大学学报, 2022, 56(10): 1912-1923.
7. Jun T J, Nguyen H M, Kang D, et al. ECG arrhythmia classification using a 2-D convolutional neural network. arXiv, 2018: 1804.06812.
8. Huang J, Chen B, Yao B, et al. ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access, 2019, 7: 92871-92880.
9. Ullah A, Anwar S M, Bilal M, et al. Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation. Remote Sensing, 2020, 12(10): 1685.
10. 李鸿强, 吴非凡, 曹路, 等. 基于改进深度残差网络的心电信号分类算法. 天津工业大学学报, 2022, 41(5): 65-72.
11. 刘晋瑞, 宋婷, 舒智林, 等. 一种面向运动解码的EEG-fNIRS时频特征融合与协同分类方法. 仪器仪表学报, 2022, 43(7): 165-173.
12. Ahmad Z, Tabassum A, Guan L, et al. ECG heartbeat classification using multimodal fusion. IEEE Access, 2021, 9: 100615-100626.
13. Shukla N, Pandey A, Shukla A P, et al. ECG-ViT: A transformer-based ECG classifier for energy-constraint wearable devices. J Sensors, 2022, 2022: 1-9.
14. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv, 2010: 2010.11929.
15. Wu H, Xiao B, Codella N, et al. Cvt: Introducing convolutions to vision transformers// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 22-31.
16. Moody G B, Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol, 2001, 20(3): 45-50.
17. Bousseljot R, Kreiseler D, Schnabel A. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomedizinische Technik/Biomedical Engineering, 1995, 40(s1): 317-318.
18. Kachuee M, Fazeli S, Sarrafzadeh M. ECG heartbeat classification: A deep transferable representation// International Conference on Healthcare Informatics (ICHI). New York: IEEE, 2018: 443-444.
19. 古莹奎, 吴宽, 李成. 基于格拉姆角场和迁移深度残差神经网络的滚动轴承故障诊断. 振动与冲击, 2022, 41(21): 228-237.
20. 张二华, 单德山, 李乔. 基于多尺度递归图理论的桥梁微弱信号非线性非平稳检验. 振动与冲击, 2019, 38(16): 123-128.
21. 何浩祥, 王玮, 黄磊. 基于卷积神经网络和递归图的桥梁损伤智能识别. 应用基础与工程科学学报, 2020, 28(4): 966-980.
22. 柯丽, 王丹妮, 杜强, 等. 基于卷积长短时记忆网络的心律失常分类方法. 电子与信息学报, 2020, 42(8): 1990-1998.
23. Acharya U R, Fujita H, Oh S L, et al. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inform Sciences, 2017, 415: 190-198.
24. Li W, Tang Y M, Yu K M, et al. SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis. Inform Sciences, 2022, 589: 738-750.

Journal of Biomedical Engineering

Electrocardiogram classification algorithm based on CvT-13 and multimodal image fusion

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