Automatic detection and visualization of myocardial infarction in electrocardiograms based on an interpretable deep learning model_Journal of Biomedical Engineering

Authors：

ZHANG Yue ¹ , ZHANG Yifei ¹ , XIE Baojie ¹ ,  LAI Dakun ¹

1. University of Electronic Science and Technology of China, School of Electronic Science and Engineering, Chengdu SiChuan 100084, P. R. China;

Corresponding author：

LAI Dakun, Email: dklai@uestc.edu.cn

Keywords：

Myocardial infarction; Deep learning; Electrocardiogram; Interpretability; Characteristics of electrocardiogram waves

DOI：

10.7507/1001-5515.202503016

Video：

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Automated detection of myocardial infarction (MI) is crucial for preventing sudden cardiac death and enabling early intervention in cardiovascular diseases. This paper proposes a deep learning framework based on a lightweight convolutional neural network (CNN) combined with one-dimensional gradient-weighted class activation mapping (1D Grad-CAM) for the automated detection of MI and the visualization of key waveform features in single-lead electrocardiograms (ECGs). The proposed method was evaluated using a total of 432 records from the Physikalisch-Technische Bundesanstalt Diagnostic ECG Database (PTBDB) and the Normal Sinus Rhythm Database (NSRDB), comprising 334 MI and 98 normal ECGs. Experimental results demonstrated that the model achieved an accuracy, sensitivity, and specificity of 95.75%, 96.03%, and 95.47%, respectively, in MI detection. Furthermore, the visualization results indicated that the model’s decision-making process aligned closely with clinically critical features, including pathological Q waves, ST-segment elevation, and T-wave inversion. This study confirms that the proposed deep learning algorithm combined with explainable technology performs effectively in the intelligent diagnosis of MI and the visualization of critical ECG waveforms, demonstrating its potential as a useful tool for early MI risk assessment and computer-aided diagnosis.

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29.	Lai D, Bu Y, Su Y, et al. Non-standardized patch-based ECG lead together with deep learning based algorithm for automatic screening of atrial fibrillation. IEEE J Biomed Health Inform, 2020, 24(6): 1569-1578.
30.	姜宇琛, 张月, 茶兴增, 等. 基于多层丝网印刷柔性生物电干电极的单导联心电胸带. 中国生物医学工程学报, 2023, 42(5): 636-640.
31.	Bu Y, Hassan M F U, Lai D. The embedding of flexible conductive silver-coated electrodes into ECG monitoring garment for minimizing motion artefacts. IEEE Sens J, 2020, 21(13): 14454-14465.

1. Choudhary P S, Dandapat S. Multibranch 1D CNN for detection and localization of myocardial infarction from 12 lead electrocardiogram signa// 2021 IEEE 18th India Council International Conference (INDICON). Guwahati: IEEE, 2021: 1-5.
2. 万学红, 卢雪峰. 诊断学. 第9版. 北京: 人民卫生出版社, 2018: 501-514.
3. Arif M, Malagore I A, Afsar F A. Detection and localization of myocardial infarction using k-nearest neighbor classifier. J Med Syst, 2012, 36(1): 279-289.
4. Acharya U R, Fujita H, Adam M, et al. Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study. Inf Sci, 2017, 377: 17-29.
5. Fatimah B, Singh P, Singhal A, et al. Efficient detection of myocardial infarction from single lead ECG signal. Biomed Signal Process Control, 2021, 68: 102678.
6. Bhaskar N A. Performance analysis of support vector machine and neural networks in detection of myocardial infarction. Proced Comput Sci, 2015, 46: 20-30.
7. Lin Z, Gao Y, Chen Y, et al. Automated detection of myocardial infarction using robust features extracted from 12-lead ECG. Signal Image Video P, 2020, 14: 857-865.
8. Sraitih M, Jabrane Y, El Hassani A H. A robustness evaluation of machine learning algorithms for ECG myocardial infarction detection. J Clin Med, 2022, 11(17): 4935.
9. Khan R. H, Miah J., Nipun S. A., et al. A comparative study of machine learning classifiers to analyze the precision of myocardial infarction prediction// 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC). Las VegasA: IEEE, 2023: 949-954.
10. 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. Inf Sci, 2017, 415: 190-198.
11. Liu W, Huang Q, Chang S, et al. Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Signal Process Control, 2018, 45: 22-32.
12. Jian J Z, Ger T R, Lai H H, et al. Detection of myocardial infarction using ECG and multi-scale feature concatenate. Sensors, 2021, 21(5): 1906.
13. Liu W, Zhang M, Zhang Y, et al. Real-time multilead convolutional neural network for myocardial infarction detection. IEEE J Biomed Health Inform, 2017, 22(5): 1434-1444.
14. Strodthoff N, Strodthoff C. Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol Meas, 2019, 40(1): 015001.
15. Han C, Shi L. ML–ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG. Comput Meth Prog Bio, 2020, 185: 105138.
16. Cao Y, Liu W, Zhang S, et al. Detection and localization of myocardial infarction based on multi-scale resnet and attention mechanism. Front Physiol, 2022, 13: 24.
17. Baloglu U B, Talo M, Yildirim O, et al. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recogn Lett, 2019, 122: 23-30.
18. Feng K, Pi X, Liu H, et al. Myocardial infarction classification based on convolutional neural network and recurrent neural network. Appl Sci, 2019, 9(9): 1879.
19. Ullah A, Anwar S M, Bilal M, et al. Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation. Remote Sens, 2020, 12(10): 1685.
20. Jikui L, Ruxin W, Bo W, et al. Myocardial infarction detection and localization with electrocardiogram based on convolutional neural network. Chin J Electron, 2021, 30(5): 833-842.
21. Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2921-2929.
22. Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization// Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 618-626.
23. Panwar H, Gupta P K, Siddiqui M K, et al. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos Solitons Fractals, 2020, 140: 110190.
24. Goldberger A L, Amaral L A N, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): 215-220.
25. Moody G B, Mark R G. The MIT-BIH arrhythmia database on CD-ROM and software for use with it// Proceedings Computers in Cardiology. Chicago: IEEE, 1990: 185-188.
26. Kumar A, Tomar H, Mehla V K, et al. Stationary wavelet transform based ECG signal denoising method. ISA Trans, 2021, 114: 251-262.
27. Kayikcioglu I, Akdeniz F, Köse C, et al. Time-frequency approach to ECG classification of myocardial infarction. Comput Electr Eng, 2020, 84: 106621.
28. Rashid N, Al Faruque M A. Energy-efficient real-time myocardial infarction detection on wearable devices// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 4648-4651.
29. Lai D, Bu Y, Su Y, et al. Non-standardized patch-based ECG lead together with deep learning based algorithm for automatic screening of atrial fibrillation. IEEE J Biomed Health Inform, 2020, 24(6): 1569-1578.
30. 姜宇琛, 张月, 茶兴增, 等. 基于多层丝网印刷柔性生物电干电极的单导联心电胸带. 中国生物医学工程学报, 2023, 42(5): 636-640.
31. Bu Y, Hassan M F U, Lai D. The embedding of flexible conductive silver-coated electrodes into ECG monitoring garment for minimizing motion artefacts. IEEE Sens J, 2020, 21(13): 14454-14465.

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