Advances in artificial intelligence in prediction of atrial fibrillation_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

HE Kang ,  WU Zhong

Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P.R.China;

Corresponding?author：

WU Zhong, Email: wuzhong@wchscu.cn

Keywords：

Artificial intelligence; atrial fibrillation; electrocardiogram; review

DOI：

10.7507/1007-4848.202005077

Video：

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Atrial fibrillation (AF) is one of the most common arrhythmias. Today, there are a large number of AF patients worldwide, and incidence increases with the increase of age. However, the current diagnosis rate of AF via auxiliary examination is relatively low. In view of the widespread application of artificial intelligence (AI) in the medical field, the diagnosis of AF using AI has also become a research hotspot. This article briefly introduces the relevant aspects of AI and reviews the application of AI in AF prediction.

Citation： HE Kang, WU Zhong. Advances in artificial intelligence in prediction of atrial fibrillation. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2020, 27(12): 1472-1478. doi: 10.7507/1007-4848.202005077 Copy

1.	黃從新, 張澍, 黃德嘉, 等. 心房顫動: 目前的認識和治療建議 (2018). 中華心律失常學雜志, 2018, 32(4): 279-346.
2.	Cai WJ, Chen YD, Guo J, et al. Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network. Comput Biol Med, 2020, 116: 103378.
3.	孟小峰, 慈祥. 大數據管理: 概念, 技術與挑戰. 計算機研究與發展, 2013, 50(1): 146-169.
4.	Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol, 2018, 71(23): 2668-2679.
5.	Lip GY, Nieuwlaat R, Pisters R, et al. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest, 2010, 137(2): 263-272.
6.	Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
7.	尹寶才, 王文通, 王立春. 深度學習研究綜述. 北京工業大學學報, 2015, 41(1): 48-59.
8.	郭麗麗, 丁世飛. 深度學習研究進展. 計算機科學, 2015, 42(5): 28-33.
9.	Abiodun OI, Jantan A, Omolara AE, et al. State-of-the-art in artificial neural network applications: A survey. Heliyon, 2018, 4(11): e00938.
10.	Dorado-Díaz PI, Sampedro-Gómez J, Vicente-Palacios V, et al. Applications of artificial intelligence in cardiology. The future is already here. Rev Esp Cardiol (Engl Ed), 2019, 72(12): 1065-1075.
11.	周飛燕, 金林鵬, 董軍. 卷積神經網絡研究綜述. 計算機學報, 2017, 40(6): 1229-1251.
12.	Rizwan IRI, Neubert J. Deep learning approaches to biomedical image segmentation. Inform Med Unloc, 2020. [Epub ahead of print].
13.	劉建偉, 劉媛, 羅雄麟. 深度學習研究進展. 計算機應用研究, 2014, 31(7): 1921-1930.
14.	沈榮, 張保文. 機器學習學習方式及其算法探討. 電腦知識與技術, 2017, 13(23): 159-160.
15.	殷瑞剛, 魏帥, 李晗, 等. 深度學習中的無監督學習方法綜述. 計算機系統應用, 2016, 25(8): 1-7.
16.	Kottkamp H. Human atrial fibrillation substrate: towards a specific fibrotic atrial cardiomyopathy. Eur Heart J, 2013, 34(35): 2731-2738.
17.	Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
18.	Bellotti P, Spirito P, Lupi G, et al. Left atrial appendage function assessed by transesophageal echocardiography before and on the day after elective cardioversion for nonvalvular atrial fibrillation. Am J Cardiol, 1998, 81(10): 1199-1202.
19.	Warraich HJ, Gandhavadi M, Manning WJ. Mechanical discordance of the left atrium and appendage: a novel mechanism of stroke in paroxysmal atrial fibrillation. Stroke, 2014, 45(5): 1481-1484.
20.	Dai HH, Yin LY, Li Y. QRS residual removal in atrial activity signals extracted from single lead: a new perspective based on signal extrapolation. IET Sig Proc, 2016, 10(9): 1169-1175.
21.	Khamis H, Chen J, Stephen Redmond J, et al. Detection of atrial fibrillation from RR intervals and PQRST morphology using a neural network ensemble. Conf Proc IEEE Eng Med Biol Soc, 2018, 2018: 5998-6001.
22.	Chesnokov YV. Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artif Intell Med, 2008, 43(2): 151-165.
23.	Christov I, Krasteva V, Simova I, et al. Ranking of the most reliable beat morphology and heart rate variability features for the detection of atrial fibrillation in short single-lead ECG. Physio Meas, 2018, 39(9): 094005.
24.	Henzel N, Wrobel J, Horoba K. Atrial fibrillation episodes detection based on classification of heart rate derived features. Proceedings of the 24th International Conference Mixed Design of Integrated Circuits and Systems. Poland, 2017.
25.	Boon KH, Khalil-Hani M, Malarvili MB. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm Ⅲ. Comput Methods Programs Biomed, 2018, 153: 171-184.
26.	Fan X, Yao Q, Cai Y, et al. Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordings. IEEE J Biomed Health Inform, 2018, 22(6): 1744-1753.
27.	Yao QH, Wang RX, Fan XM, et al. Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network. Information Fusion, 2020, 53: 174-182.
28.	Chen TM, Huang CH, Shih ESC, et al. Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model. iScience, 2020, 23(3): 100886.
29.	Pimor A, Galli E, Vitel E, et al. Predictors of post-operative cardiovascular events, focused on atrial fibrillation, after valve surgery for primary mitral regurgitation. Eur Heart J Cardiovasc Imaging, 2019, 20(2): 177-184.
30.	Budzianowski J, Hiczkiewicz J, Burchardt P, et al. Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms. Heart Vessels, 2019, 34(2): 352-359.
31.	Isakadze N, Martin SS. How useful is the smartwatch ECG? Trends Cardiovasc Med, 2020, 30(7): 442-448.
32.	Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med, 2019, 381(20): 1909-1917.
33.	Wasserlauf J, You C, Patel R, et al. Smartwatch performance for the detection and quantification of atrial fibrillation. Circ Arrhythm Electrophysiol, 2019, 12(6): e006834.
34.	Yan BP, Lai WHS, Chan CKY, et al. High-throughput, contact-free detection of atrial fibrillation from video with deep learning. JAMA Cardiol, 2020, 5(1): 105-107.

1. 黃從新, 張澍, 黃德嘉, 等. 心房顫動: 目前的認識和治療建議 (2018). 中華心律失常學雜志, 2018, 32(4): 279-346.
2. Cai WJ, Chen YD, Guo J, et al. Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network. Comput Biol Med, 2020, 116: 103378.
3. 孟小峰, 慈祥. 大數據管理: 概念, 技術與挑戰. 計算機研究與發展, 2013, 50(1): 146-169.
4. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol, 2018, 71(23): 2668-2679.
5. Lip GY, Nieuwlaat R, Pisters R, et al. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest, 2010, 137(2): 263-272.
6. Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
7. 尹寶才, 王文通, 王立春. 深度學習研究綜述. 北京工業大學學報, 2015, 41(1): 48-59.
8. 郭麗麗, 丁世飛. 深度學習研究進展. 計算機科學, 2015, 42(5): 28-33.
9. Abiodun OI, Jantan A, Omolara AE, et al. State-of-the-art in artificial neural network applications: A survey. Heliyon, 2018, 4(11): e00938.
10. Dorado-Díaz PI, Sampedro-Gómez J, Vicente-Palacios V, et al. Applications of artificial intelligence in cardiology. The future is already here. Rev Esp Cardiol (Engl Ed), 2019, 72(12): 1065-1075.
11. 周飛燕, 金林鵬, 董軍. 卷積神經網絡研究綜述. 計算機學報, 2017, 40(6): 1229-1251.
12. Rizwan IRI, Neubert J. Deep learning approaches to biomedical image segmentation. Inform Med Unloc, 2020. [Epub ahead of print].
13. 劉建偉, 劉媛, 羅雄麟. 深度學習研究進展. 計算機應用研究, 2014, 31(7): 1921-1930.
14. 沈榮, 張保文. 機器學習學習方式及其算法探討. 電腦知識與技術, 2017, 13(23): 159-160.
15. 殷瑞剛, 魏帥, 李晗, 等. 深度學習中的無監督學習方法綜述. 計算機系統應用, 2016, 25(8): 1-7.
16. Kottkamp H. Human atrial fibrillation substrate: towards a specific fibrotic atrial cardiomyopathy. Eur Heart J, 2013, 34(35): 2731-2738.
17. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
18. Bellotti P, Spirito P, Lupi G, et al. Left atrial appendage function assessed by transesophageal echocardiography before and on the day after elective cardioversion for nonvalvular atrial fibrillation. Am J Cardiol, 1998, 81(10): 1199-1202.
19. Warraich HJ, Gandhavadi M, Manning WJ. Mechanical discordance of the left atrium and appendage: a novel mechanism of stroke in paroxysmal atrial fibrillation. Stroke, 2014, 45(5): 1481-1484.
20. Dai HH, Yin LY, Li Y. QRS residual removal in atrial activity signals extracted from single lead: a new perspective based on signal extrapolation. IET Sig Proc, 2016, 10(9): 1169-1175.
21. Khamis H, Chen J, Stephen Redmond J, et al. Detection of atrial fibrillation from RR intervals and PQRST morphology using a neural network ensemble. Conf Proc IEEE Eng Med Biol Soc, 2018, 2018: 5998-6001.
22. Chesnokov YV. Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artif Intell Med, 2008, 43(2): 151-165.
23. Christov I, Krasteva V, Simova I, et al. Ranking of the most reliable beat morphology and heart rate variability features for the detection of atrial fibrillation in short single-lead ECG. Physio Meas, 2018, 39(9): 094005.
24. Henzel N, Wrobel J, Horoba K. Atrial fibrillation episodes detection based on classification of heart rate derived features. Proceedings of the 24th International Conference Mixed Design of Integrated Circuits and Systems. Poland, 2017.
25. Boon KH, Khalil-Hani M, Malarvili MB. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm Ⅲ. Comput Methods Programs Biomed, 2018, 153: 171-184.
26. Fan X, Yao Q, Cai Y, et al. Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordings. IEEE J Biomed Health Inform, 2018, 22(6): 1744-1753.
27. Yao QH, Wang RX, Fan XM, et al. Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network. Information Fusion, 2020, 53: 174-182.
28. Chen TM, Huang CH, Shih ESC, et al. Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model. iScience, 2020, 23(3): 100886.
29. Pimor A, Galli E, Vitel E, et al. Predictors of post-operative cardiovascular events, focused on atrial fibrillation, after valve surgery for primary mitral regurgitation. Eur Heart J Cardiovasc Imaging, 2019, 20(2): 177-184.
30. Budzianowski J, Hiczkiewicz J, Burchardt P, et al. Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms. Heart Vessels, 2019, 34(2): 352-359.
31. Isakadze N, Martin SS. How useful is the smartwatch ECG? Trends Cardiovasc Med, 2020, 30(7): 442-448.
32. Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med, 2019, 381(20): 1909-1917.
33. Wasserlauf J, You C, Patel R, et al. Smartwatch performance for the detection and quantification of atrial fibrillation. Circ Arrhythm Electrophysiol, 2019, 12(6): e006834.
34. Yan BP, Lai WHS, Chan CKY, et al. High-throughput, contact-free detection of atrial fibrillation from video with deep learning. JAMA Cardiol, 2020, 5(1): 105-107.

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Advances in artificial intelligence in prediction of atrial fibrillation

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