A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction_Journal of Biomedical Engineering

Authors：

HAN Chuang ¹ , QUE Wenge ² , WANG Zhizhong ³ , WANG Songwei ³ , LI Yanting ¹ ,  SHI Li ²

1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, P. R. China;
2. Department of Automation, Tsinghua university, Beijing 100000, P. R. China;
3. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450000, P. R. China;

Corresponding author：

Keywords：

Electrocardiogram; Myocardial infarction; Intelligent auxiliary diagnosis; Feature extraction; Machine learning

DOI：

10.7507/1001-5515.202212010

Video：

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Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.

Citation： HAN Chuang, QUE Wenge, WANG Zhizhong, WANG Songwei, LI Yanting, SHI Li. A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction. Journal of Biomedical Engineering, 2023, 40(5): 1019-1026. doi: 10.7507/1001-5515.202212010 Copy

1.	马丽媛, 王增武, 樊静, 等. 《中国心血管健康与疾病报告2021》要点解读. 中国全科医学, 2022, 25(27): 3331-3346.
2.	Vogel B, Claessen B E, Arnold S V, et al. ST-segment elevation myocardial infarction. Nature Reviews Disease Primers, 2019, 5(1): 39.
3.	He R, Liu Y, Wang K, et al. Automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. IEEE Journal of Biomedical and Health Informatics, 2021, 25(4): 1052-1061.
4.	Liu Lingfeng, Bai Baodan, Chen Xinrong, et al. Semantic segmentation of QRS complex in single channel ECG with bidirectional LSTM networks. Journal of Medical Imaging and Health Informatics, 2020, 10(3): 758-762.
5.	Peimankar A, Puthusserypady S. DENS-ECG: A deep learning approach for ECG signal delineation. Expert Systems with Applications, 2021, 165: 113911.
6.	刘近贞, 孙利飞, 熊慧, 等. 基于能量分段与平稳小波变换的运动心电图特征波检测算法研究. 生物医学工程学杂志, 2021, 38(6): 1181-1192.
7.	Londhe A N, Atulkar M. Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM. Biomedical Signal Processing and Control, 2021, 63: 102162.
8.	Han C, Que W, Wang S, et al. QRS complexes and T waves localization in multi-lead ECG signals based on deep learning and electrophysiology knowledge. Expert Systems with Applications, 2022, 199: 117187.
9.	周飞燕. 心电图分析的多分类器融合及其评价方法研究. 合肥: 中国科学技术大学,2017.
10.	杜海曼, 边婷, 熊鹏, 等. 基于支持向量机多特征融合ST段形态分类. 生物医学工程学杂志, 2022, 39(4): 702-712.
11.	谢佳静, 魏守水, 江兴娥, 等. 基于CNN和频率切片小波变换的T波形态分类. 中国生物医学工程学报, 2021, 40(1): 1-11.
12.	熊鹏, 齐明锐, 张杰烁, 等. 基于形态学特征的下壁心肌梗死检测. 生物医学工程学杂志, 2021, 38(1): 65-71.
13.	Swain S S, Patra D, Singh Y O. Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis. Biocybernetics and Biomedical Engineering, 2020, 40(3): 1174-1189.
14.	Dohare A K, Kumar V, Kumar R. Detection of myocardial infarction in 12 lead ECG using support vector machine. Applied Soft Computing, 2018, 64: 138-147.
15.	Lin Z, Gao Y, Chen Y, et al. Automated detection of myocardial infarction using robust features extracted from 12-lead ECG. Signal, Image and Video Processing, 2020, 14: 857-865.
16.	Fatimah B, Singh P, Singhal A, et al. Efficient detection of myocardial infarction from single lead ECG signal. Biomedical Signal Processing and Control, 2021, 68: 102678.
17.	Zhang J, Liu M, Xiong P, et al. Automated localization of myocardial infarction of image-based multilead ECG tensor with Tucker2 decomposition. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-15.
18.	Zhang J, Liu M, Xiong P, et al. Automated localization of myocardial infarction from vectorcardiographic via tensor decomposition. IEEE Transactions on Biomedical Engineering, 2023, 70(3): 812-823.
19.	Zhang J, Liu M, Xiong P, et al. A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction. Engineering Applications of Artificial Intelligence, 2021, 97: 104092.
20.	Sun Q, Liang C, Chen T, et al. Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning. Computer Methods and Programs in Biomedicine, 2022, 226: 107124.
21.	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. Information Sciences, 2017, 415-416: 190-198.
22.	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.
23.	Pan W, An Y, Guan Y, et al. MCA-net: a multi-task channel attention network for myocardial infarction detection and location using 12-lead ECGs. Computers in Biology and Medicine, 2022, 150: 106199.
24.	Han C, Shi L. ML–ResNet: a novel network to detect and locate myocardial infarction using 12 leads ECG. Computer Methods and Programs in Biomedicine, 2020, 185: 105138.
25.	Cao Y, Liu W, Zhang S, et al. Detection and localization of myocardial infarction based on multi-scale ResNet and attention mechanism. Frontiers in Physiology, 2022, 13: 783184.
26.	Xiong P, Xue Y, Zhang J, et al. Localization of myocardial infarction with multi-lead ECG based on DenseNet. Computer Methods and Programs in Biomedicine, 2021, 203: 106024.
27.	熊鹏, 薛彦平, 刘明, 等. 基于密集连接卷积神经网络的下壁心肌梗死检测. 生物医学工程学杂志, 2020, 37(1): 142-149.
28.	Jahmunah V, Ng E Y K, Tan R S, et al. Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals. Computers in Biology and Medicine, 2022, 146: 105550.
29.	Sugimoto K, Kon Y, Lee S, et al. Detection and localization of myocardial infarction based on a convolutional autoencoder. Knowledge-Based Systems, 2019, 178: 123-131.
30.	徐文畅, 何文明, 游斌权, 等. 基于形态特征提取的急性下壁心肌梗死BiLSTM网络辅助诊断算法. 电子与信息学报, 2021, 43(9): 2561-2568.
31.	Liu W, Wang F, Huang Q, et al. MFB-CBRNN: a hybrid network for MI detection using 12-lead ECGs. IEEE Journal of Biomedical and Health Informatics, 2020, 24(2): 503-514.
32.	Rai H M, Chatterjee K. Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data. Applied Intelligence, 2022, 52(5): 5366-5384.
33.	Fu L, Lu B, Nie B, et al. Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals. Sensors, 2020, 20(4): 1020.
34.	He Z, Yuan S, Zhao J, et al. A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning. Information Sciences, 2022, 606: 649-668.
35.	He Z, Yuan S, Zhao J, et al. A robust myocardial infarction localization system based on multi-branch residual shrinkage network and active learning with clustering. Biomedical Signal Processing and Control, 2023, 80: 104238.
36.	Prabhakararao E, Dandapat S. Myocardial infarction severity stages classification from ECG signals using attentional recurrent neural network. IEEE Sensors Journal, 2020, 20(15): 8711-8720.
37.	Tadesse G A, Javed H, Weldemariam K, et al. DeepMI: deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time. Artificial Intelligence in Medicine, 2021, 121: 102192.
38.	Liu J, Wang R, Wen B, et al. Myocardial infarction detection and localization with electrocardiogram based on convolutional neural network. Chinese Journal of Electronics, 2021, 30(5): 833-842.
39.	He C, Liu M, Xiong P, et al. Localization of myocardial infarction using a multi-branch weight sharing network based on 2-D vectorcardiogram. Engineering Applications of Artificial Intelligence, 2022, 116: 105428.
40.	Hao P, Gao X, Li Z, et al. Multi-branch fusion network for myocardial infarction screening from 12-lead ECG images. Computer Methods and Programs in Biomedicine, 2020, 184: 105286.
41.	Anand A, Kadian T, Shetty M K, et al. Explainable AI decision model for ECG data of cardiac disorders. Biomedical Signal Processing and Control, 2022, 75: 103584.
42.	Sharma M, Tan R S, Acharya U R. A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Computers in Biology and Medicine, 2018, 102: 341-356.
43.	Sharma L D, Sunkaria R K. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal, Image and Video Processing, 2018, 12(2): 199-206.
44.	Fang R, Lu C C, Chuang C T, et al. A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification. Computer Methods and Programs in Biomedicine, 2022, 219: 106762.
45.	Ribeiro A H, Ribeiro M H, Paixão G M M, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nature Communications, 2020, 11(1): 1760.
46.	Zhu H, Cheng C, Yin H, et al. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. The Lancet Digital Health, 2020, 2(7): e348-e357.

1. 马丽媛, 王增武, 樊静, 等. 《中国心血管健康与疾病报告2021》要点解读. 中国全科医学, 2022, 25(27): 3331-3346.
2. Vogel B, Claessen B E, Arnold S V, et al. ST-segment elevation myocardial infarction. Nature Reviews Disease Primers, 2019, 5(1): 39.
3. He R, Liu Y, Wang K, et al. Automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. IEEE Journal of Biomedical and Health Informatics, 2021, 25(4): 1052-1061.
4. Liu Lingfeng, Bai Baodan, Chen Xinrong, et al. Semantic segmentation of QRS complex in single channel ECG with bidirectional LSTM networks. Journal of Medical Imaging and Health Informatics, 2020, 10(3): 758-762.
5. Peimankar A, Puthusserypady S. DENS-ECG: A deep learning approach for ECG signal delineation. Expert Systems with Applications, 2021, 165: 113911.
6. 刘近贞, 孙利飞, 熊慧, 等. 基于能量分段与平稳小波变换的运动心电图特征波检测算法研究. 生物医学工程学杂志, 2021, 38(6): 1181-1192.
7. Londhe A N, Atulkar M. Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM. Biomedical Signal Processing and Control, 2021, 63: 102162.
8. Han C, Que W, Wang S, et al. QRS complexes and T waves localization in multi-lead ECG signals based on deep learning and electrophysiology knowledge. Expert Systems with Applications, 2022, 199: 117187.
9. 周飞燕. 心电图分析的多分类器融合及其评价方法研究. 合肥: 中国科学技术大学,2017.
10. 杜海曼, 边婷, 熊鹏, 等. 基于支持向量机多特征融合ST段形态分类. 生物医学工程学杂志, 2022, 39(4): 702-712.
11. 谢佳静, 魏守水, 江兴娥, 等. 基于CNN和频率切片小波变换的T波形态分类. 中国生物医学工程学报, 2021, 40(1): 1-11.
12. 熊鹏, 齐明锐, 张杰烁, 等. 基于形态学特征的下壁心肌梗死检测. 生物医学工程学杂志, 2021, 38(1): 65-71.
13. Swain S S, Patra D, Singh Y O. Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis. Biocybernetics and Biomedical Engineering, 2020, 40(3): 1174-1189.
14. Dohare A K, Kumar V, Kumar R. Detection of myocardial infarction in 12 lead ECG using support vector machine. Applied Soft Computing, 2018, 64: 138-147.
15. Lin Z, Gao Y, Chen Y, et al. Automated detection of myocardial infarction using robust features extracted from 12-lead ECG. Signal, Image and Video Processing, 2020, 14: 857-865.
16. Fatimah B, Singh P, Singhal A, et al. Efficient detection of myocardial infarction from single lead ECG signal. Biomedical Signal Processing and Control, 2021, 68: 102678.
17. Zhang J, Liu M, Xiong P, et al. Automated localization of myocardial infarction of image-based multilead ECG tensor with Tucker2 decomposition. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-15.
18. Zhang J, Liu M, Xiong P, et al. Automated localization of myocardial infarction from vectorcardiographic via tensor decomposition. IEEE Transactions on Biomedical Engineering, 2023, 70(3): 812-823.
19. Zhang J, Liu M, Xiong P, et al. A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction. Engineering Applications of Artificial Intelligence, 2021, 97: 104092.
20. Sun Q, Liang C, Chen T, et al. Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning. Computer Methods and Programs in Biomedicine, 2022, 226: 107124.
21. 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. Information Sciences, 2017, 415-416: 190-198.
22. 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.
23. Pan W, An Y, Guan Y, et al. MCA-net: a multi-task channel attention network for myocardial infarction detection and location using 12-lead ECGs. Computers in Biology and Medicine, 2022, 150: 106199.
24. Han C, Shi L. ML–ResNet: a novel network to detect and locate myocardial infarction using 12 leads ECG. Computer Methods and Programs in Biomedicine, 2020, 185: 105138.
25. Cao Y, Liu W, Zhang S, et al. Detection and localization of myocardial infarction based on multi-scale ResNet and attention mechanism. Frontiers in Physiology, 2022, 13: 783184.
26. Xiong P, Xue Y, Zhang J, et al. Localization of myocardial infarction with multi-lead ECG based on DenseNet. Computer Methods and Programs in Biomedicine, 2021, 203: 106024.
27. 熊鹏, 薛彦平, 刘明, 等. 基于密集连接卷积神经网络的下壁心肌梗死检测. 生物医学工程学杂志, 2020, 37(1): 142-149.
28. Jahmunah V, Ng E Y K, Tan R S, et al. Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals. Computers in Biology and Medicine, 2022, 146: 105550.
29. Sugimoto K, Kon Y, Lee S, et al. Detection and localization of myocardial infarction based on a convolutional autoencoder. Knowledge-Based Systems, 2019, 178: 123-131.
30. 徐文畅, 何文明, 游斌权, 等. 基于形态特征提取的急性下壁心肌梗死BiLSTM网络辅助诊断算法. 电子与信息学报, 2021, 43(9): 2561-2568.
31. Liu W, Wang F, Huang Q, et al. MFB-CBRNN: a hybrid network for MI detection using 12-lead ECGs. IEEE Journal of Biomedical and Health Informatics, 2020, 24(2): 503-514.
32. Rai H M, Chatterjee K. Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data. Applied Intelligence, 2022, 52(5): 5366-5384.
33. Fu L, Lu B, Nie B, et al. Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals. Sensors, 2020, 20(4): 1020.
34. He Z, Yuan S, Zhao J, et al. A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning. Information Sciences, 2022, 606: 649-668.
35. He Z, Yuan S, Zhao J, et al. A robust myocardial infarction localization system based on multi-branch residual shrinkage network and active learning with clustering. Biomedical Signal Processing and Control, 2023, 80: 104238.
36. Prabhakararao E, Dandapat S. Myocardial infarction severity stages classification from ECG signals using attentional recurrent neural network. IEEE Sensors Journal, 2020, 20(15): 8711-8720.
37. Tadesse G A, Javed H, Weldemariam K, et al. DeepMI: deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time. Artificial Intelligence in Medicine, 2021, 121: 102192.
38. Liu J, Wang R, Wen B, et al. Myocardial infarction detection and localization with electrocardiogram based on convolutional neural network. Chinese Journal of Electronics, 2021, 30(5): 833-842.
39. He C, Liu M, Xiong P, et al. Localization of myocardial infarction using a multi-branch weight sharing network based on 2-D vectorcardiogram. Engineering Applications of Artificial Intelligence, 2022, 116: 105428.
40. Hao P, Gao X, Li Z, et al. Multi-branch fusion network for myocardial infarction screening from 12-lead ECG images. Computer Methods and Programs in Biomedicine, 2020, 184: 105286.
41. Anand A, Kadian T, Shetty M K, et al. Explainable AI decision model for ECG data of cardiac disorders. Biomedical Signal Processing and Control, 2022, 75: 103584.
42. Sharma M, Tan R S, Acharya U R. A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Computers in Biology and Medicine, 2018, 102: 341-356.
43. Sharma L D, Sunkaria R K. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal, Image and Video Processing, 2018, 12(2): 199-206.
44. Fang R, Lu C C, Chuang C T, et al. A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification. Computer Methods and Programs in Biomedicine, 2022, 219: 106762.
45. Ribeiro A H, Ribeiro M H, Paixão G M M, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nature Communications, 2020, 11(1): 1760.
46. Zhu H, Cheng C, Yin H, et al. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. The Lancet Digital Health, 2020, 2(7): e348-e357.

Journal of Biomedical Engineering

A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction

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