Application of deep neural network models to the electrocardiogram_West China Medical Journal

Authors：

 ZHOU Tian

Department of Electrocardiogram, the First College of Clinical Medical Science, China Three Gorges University, Yichang Central People’s Hospital, Yichang, Hubei 443000, P. R. China;

Corresponding author：

ZHOU Tian, Email: 466694791@qq.com

Keywords：

Deep neural network models; electrocardiogram; artificial intelligence

DOI：

10.7507/1002-0179.202210146

Video：

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Electrocardiogram (ECG) is a noninvasive, inexpensive, and convenient test for diagnosing cardiovascular diseases and assessing the risk of cardiovascular events. Although there are clear standardized operations and procedures for ECG examination, the interpretation of ECG by even trained physicians can be biased due to differences in diagnostic experience. In recent years, artificial intelligence has become a powerful tool to automatically analyze medical data by building deep neural network models, and has been widely used in the field of medical image diagnosis such as CT, MRI, ultrasound and ECG. This article mainly introduces the application progress of deep neural network models in ECG diagnosis and prediction of cardiovascular diseases, and discusses its limitations and application prospects.

Citation： ZHOU Tian. Application of deep neural network models to the electrocardiogram. West China Medical Journal, 2023, 38(1): 126-129. doi: 10.7507/1002-0179.202210146 Copy

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22.	Vaid A, Johnson KW, Badgeley MA, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging, 2021, 15(3): 395-410.
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27.	Gumpfer N, Grün D, Hannig J, et al. Detecting myocardial scar using electrocardiogram data and deep neural networks. Biol Chem, 2021, 402(8): 911-923.
28.	Cohen-Shelly M, Attia ZI, Friedman PA, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J, 2021, 42(30): 2885-2896.
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30.	Kwon JM, Kim KH, Akkus Z, et al. Artificial intelligence for detecting mitral regurgitation using electrocardiography. J Electrocardiol, 2020, 59: 151-157.

1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
2. Attia ZI, Harmon DM, Behr ER, et al. Application of artificial intelligence to the electrocardiogram. Eur Heart J, 2021, 42(46): 4717-4730.
3. Goto S, Goto S. Application of neural networks to 12-lead electrocardiography-current status and future directions. Circ Rep, 2019, 1(11): 481-486.
4. Ribeiro AH, Ribeiro MH, Paixão GMM, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun, 2020, 11(1): 1760.
5. Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med, 2019, 25(1): 65-69.
6. Chang KC, Hsieh PH, Wu MY, et al. Usefulness of machine learning-based detection and classification of cardiac arrhythmias with 12-lead electrocardiograms. Can J Cardiol, 2021, 37(1): 94-104.
7. Zhu H, Cheng C, Yin H, et al. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. Lancet Digit Health, 2020, 2(7): e348-e357.
8. Siontis KC, Noseworthy PA, Attia ZI, et al. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol, 2021, 18(7): 465-478.
9. Murat F, Sadak F, Yildirim O, et al. Review of deep learning-based atrial fibrillation detection studies. Int J Environ Res Public Health, 2021, 18(21): 11302.
10. Bahrami Rad A, Galloway C, Treiman D, et al. Atrial fibrillation detection in outpatient electrocardiogram monitoring: an algorithmic crowdsourcing approach. PLoS One, 2021, 16(11): e0259916.
11. Rabinstein AA, Yost MD, Faust L, et al. Artificial intelligence-enabled ECG to identify silent atrial fibrillation in embolic stroke of unknown source. J Stroke Cerebrovasc Dis, 2021, 30(9): 105998.
12. Baek YS, Lee SC, Choi W, et al. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm. Sci Rep, 2021, 11(1): 12818.
13. Suzuki S, Motogi J, Nakai H, et al. Identifying patients with atrial fibrillation during sinus rhythm on ECG: significance of the labeling in the artificial intelligence algorithm. Int J Cardiol Heart Vasc, 2022, 38: 100954.
14. Melzi P, Tolosana R, Cecconi A, et al. Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization. Sci Rep, 2021, 11(1): 22786.
15. Khurshid S, Friedman S, Reeder C, et al. ECG-based deep learning and clinical risk factors to predict atrial fibrillation. Circulation, 2022, 145(2): 122-133.
16. Senoner T, Pfeifer B, Barbieri F, et al. Identifying the location of an accessory pathway in pre-excitation syndromes using an artificial intelligence-based algorithm. J Clin Med, 2021, 10(19): 4394.
17. Zheng J, Fu G, Abudayyeh I, et al. A high-precision machine learning algorithm to classify left and right outflow tract ventricular tachycardia. Front Physiol, 2021, 12: 641066.
18. Zhao Y, Xiong J, Hou Y, et al. Early detection of ST-segment elevated myocardial infarction by artificial intelligence with 12-lead electrocardiogram. Int J Cardiol, 2020, 317: 223-230.
19. Liu WC, Lin C, Lin CS, et al. An artificial intelligence-based alarm strategy facilitates management of acute myocardial infarction. J Pers Med, 2021, 11(11): 1149.
20. Gibson CM, Mehta S, Ceschim MRS, et al. Evolution of single-lead ECG for STEMI detection using a deep learning approach. Int J Cardiol, 2022, 346: 47-52.
21. Katsushika S, Kodera S, Nakamoto M, et al. The effectiveness of a deep learning model to detect left ventricular systolic dysfunction from electrocardiograms. Int Heart J, 2021, 62(6): 1332-1341.
22. Vaid A, Johnson KW, Badgeley MA, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging, 2021, 15(3): 395-410.
23. Yao X, Rushlow DR, Inselman JW, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med, 2021, 27(5): 815-819.
24. Ko WY, Siontis KC, Attia ZI, et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol, 2020, 75(7): 722-733.
25. Siontis KC, Liu K, Bos JM, et al. Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents. Int J Cardiol, 2021, 340: 42-47.
26. Shrivastava S, Cohen-Shelly M, Attia ZI, et al. Artificial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy. Am J Cardiol, 2021, 155: 121-127.
27. Gumpfer N, Grün D, Hannig J, et al. Detecting myocardial scar using electrocardiogram data and deep neural networks. Biol Chem, 2021, 402(8): 911-923.
28. Cohen-Shelly M, Attia ZI, Friedman PA, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J, 2021, 42(30): 2885-2896.
29. Sawano S, Kodera S, Katsushika S, et al. Deep learning model to detect significant aortic regurgitation using electrocardiography. J Cardiol, 2022, 79(3): 334-341.
30. Kwon JM, Kim KH, Akkus Z, et al. Artificial intelligence for detecting mitral regurgitation using electrocardiography. J Electrocardiol, 2020, 59: 151-157.

West China Medical Journal

Application of deep neural network models to the electrocardiogram

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