Artificial intelligence in wearable electrocardiogram monitoring_Journal of Biomedical Engineering

Authors：

WANG Xingyao ^1,2 , LI Qian ^1,2 , MA Caiyun ^1,2 , ZHANG Shuo ^1,2 , LIN Yujie ^1,2 , LI Jianqing ^1,3 ,  LIU Chengyu ^1,2

1. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China;
2. State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China;
3. School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, P. R. China;

Corresponding author：

LIU Chengyu, Email: chengyu@seu.edu.cn

Keywords：

Electrocardiogram monitoring; Smart medical; Wearable electrocardiogram; Artificial intelligence

DOI：

10.7507/1001-5515.202301032

Video：

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Abstract Full text Figures/Tables Video References Cited by

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues—the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Citation： WANG Xingyao, LI Qian, MA Caiyun, ZHANG Shuo, LIN Yujie, LI Jianqing, LIU Chengyu. Artificial intelligence in wearable electrocardiogram monitoring. Journal of Biomedical Engineering, 2023, 40(6): 1084-1092, 1101. doi: 10.7507/1001-5515.202301032 Copy

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14.	Chu Q, Ouyang W, Li H, et al. Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism// 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 4846-4855..
15.	Jiang Y, Luo C, Li X, et al. Progressive reduction in gray matter in patients with schizophrenia assessed with MR imaging by using causal network analysis. Radiology, 2018, 287(2): 633-642..
16.	He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 15979-15988..
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28.	Kamath C. A new approach to detect congestive heart failure using detrended fluctuation analysis of electrocardiogram signals. J Eng Sci Technol, 2015, 10(2): 145-159..
29.	Acharya U R, Fujita H, Oh S L, et al. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Appl Intell, 2019, 49(1): 16-27..
30.	Darmawahyuni A, Nurmaini S, Yuwandini M, et al. Congestive heart failure waveform classification based on short time-step analysis with recurrent network. Inform Med Unlocked, 2020, 21: 100441..
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32.	Lee S H, Kim Y-S, Yeo M-K, et al. Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis. Sci Adv, 2022, 8(21): eabo5867..
33.	Sano A, Chen W, Lopez-Martinez D, et al. Multimodal ambulatory sleep detection using LSTM recurrent neural networks. IEEE J Biomed Health Inform, 2018, 23(4): 1607-1617..
34.	Shahin M, Ahmed B, Hamida S T-B, et al. Deep learning and insomnia: assisting clinicians with their diagnosis. IEEE J Biomed Health Inform, 2017, 21(6): 1546-1553..
35.	Ghaffari Laleh N, Truhn D, Veldhuizen G P, et al. Adversarial attacks and adversarial robustness in computational pathology. Nat Commun, 2022, 13(1): 5711..
36.	Han X, Hu Y, Foschini L, et al. Deep learning models for electrocardiograms are susceptible to adversarial attack. Nat Med, 2020, 26(3): 360-363..
37.	Vranken J F, van de Leur R R, Gupta D K, et al. Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms. Eur Heart J-Digit Health, 2021, 2(3): 401-415..
38.	Sivakumaran S, Krahn A D, Klein G J, et al. A prospective randomized comparison of loop recorders versus Holter monitors in patients with syncope or presyncope. Am J Med, 2003, 115(1): 1-5..
39.	Maron D J, Hochman J S, O'Brien S M, et al. International study of comparative health effectiveness with medical and invasive approaches (ISCHEMIA) trial: Rationale and design. Am Heart J, 2018, 201: 124-135..

1. WHO. Cardiovascular diseases (CVDs). Geneva: WHO, 2021..
2. 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2020概要. 中国循环杂志, 2021, 36(6): 521-545..
3. 刘澄玉, 杨美程, 邸佳楠, 等. 穿戴式心电: 发展历程、核心技术与未来挑战. 中国生物医学工程学报, 2019, 38(6): 641-652..
4. Tsumoto S. Automated extraction of medical expert system rules from clinical databases based on rough set theory. Inf Sci, 1998, 112: 67-84..
5. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60: 84-90..
6. Pollack C J, Sites F D, Shofer F S, et al. Application of the TIMI risk score for unstable angina and non-ST elevation acute coronary syndrome to an unselected emergency department chest pain population. Acad Emerg Med, 2006, 13(1): 13-18..
7. Hannun A Y, 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..
8. Guo Y, Wang H, Zhang H, et al. Photoplethysmography-based machine learning approaches for atrial fibrillation prediction: a report from the huawei heart study. JACC Asia, 2021, 1(3): 399-408..
9. Zhou X, Wen J, Wen Y, et al. Enhanced P wave recognition through ra-xiphoidal lead by Huawei watch ECG recording. Heart Rhythm, 2022, 19(5): 199-200..
10. Gao H, Liu C, Wang X, et al. An open-access ECG database for algorithm evaluation of QRS detection and heart rate estimation. J Med Imag Health Inform, 2019, 9(9): 1853-1858..
11. Zhou K, Liu Z, Qiao Y, et al. Domain generalization: A survey. IEEE Trans Pattern Anal Mach Intell, 2023, 45(4): 4396-4415..
12. Zhang S, Li Y, Wang X, et al. Label decoupling strategy for 12-lead ECG classification. Knowl-Based Syst, 2023, 263: 110298..
13. Liu F, Liu C, Zhao L, et al. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imag Health Inform, 2018, 8(7): 1368-1373..
14. Chu Q, Ouyang W, Li H, et al. Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism// 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 4846-4855..
15. Jiang Y, Luo C, Li X, et al. Progressive reduction in gray matter in patients with schizophrenia assessed with MR imaging by using causal network analysis. Radiology, 2018, 287(2): 633-642..
16. He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 15979-15988..
17. Liu C, Oster J, Reinertsen E, et al. A comparison of entropy approaches for AF discrimination. Physiol Meas, 2018, 39(7): 074002..
18. Ma C, Wei S, Chen T, et al. Integration of results from convolutional neural network in a support vector machine for the detection of atrial fibrillation. IEEE Trans Instrum Meas, 2021, 70: 2504610..
19. Babaeizadeh S, Gregg R E, Helfenbein E D, et al. Improvements in atrial fibrillation detection for real-time monitoring. J Electrocardiol, 2009, 42(6): 522-526..
20. Mohebbi M, Ghassemian H. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput Meth Prog Bio, 2012, 105(1): 40-49..
21. Ma C, Liu C, Wang X, et al. A multistep paroxysmal atrial fibrillation scanning strategy in long-term ECGs. IEEE Trans Instrum Meas, 2022, 71: 1-10..
22. Efremidis M, Letsas K P, Georgopoulos S, et al. Safety, long-term outcomes and predictors of recurrence following a single catheter ablation procedure for atrial fibrillation. Acta Cardiol, 2019, 74(4): 319-324..
23. Boriani G, Pettorelli D. Atrial fibrillation burden and atrial fibrillation type: clinical significance and impact on the risk of stroke and decision making for long-term anticoagulation. Vasc Pharmacol, 2016, 83: 26-35..
24. Elkin P L, Mullin S, MardekiaN J, et al. Using artificial intelligence with natural language processing to combine electronic health record’s structured and free text data to identify nonvalvular atrial fibrillation to decrease strokes and death: Evaluation and case-control study. J Med Internet Res, 2021, 23(11): e28946..
25. Behar J A, Rosenberg A A, Weiser-Bitoun I, et al. PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data. Front Physiol, 2018, 9: 1390..
26. Attia Z I, Harmon D M, Behr E R, et al. Application of artificial intelligence to the electrocardiogram. Eur Heart J, 2021, 42(46): 4717-4730..
27. Zheng J, Zhang J, Danioko S, et al. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Sci Data, 2020, 7(1): 48..
28. Kamath C. A new approach to detect congestive heart failure using detrended fluctuation analysis of electrocardiogram signals. J Eng Sci Technol, 2015, 10(2): 145-159..
29. Acharya U R, Fujita H, Oh S L, et al. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Appl Intell, 2019, 49(1): 16-27..
30. Darmawahyuni A, Nurmaini S, Yuwandini M, et al. Congestive heart failure waveform classification based on short time-step analysis with recurrent network. Inform Med Unlocked, 2020, 21: 100441..
31. Schubert C, Archer G, Zelis J M, et al. Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease. NPJ Digit Med, 2020, 3(1): 1-9..
32. Lee S H, Kim Y-S, Yeo M-K, et al. Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis. Sci Adv, 2022, 8(21): eabo5867..
33. Sano A, Chen W, Lopez-Martinez D, et al. Multimodal ambulatory sleep detection using LSTM recurrent neural networks. IEEE J Biomed Health Inform, 2018, 23(4): 1607-1617..
34. Shahin M, Ahmed B, Hamida S T-B, et al. Deep learning and insomnia: assisting clinicians with their diagnosis. IEEE J Biomed Health Inform, 2017, 21(6): 1546-1553..
35. Ghaffari Laleh N, Truhn D, Veldhuizen G P, et al. Adversarial attacks and adversarial robustness in computational pathology. Nat Commun, 2022, 13(1): 5711..
36. Han X, Hu Y, Foschini L, et al. Deep learning models for electrocardiograms are susceptible to adversarial attack. Nat Med, 2020, 26(3): 360-363..
37. Vranken J F, van de Leur R R, Gupta D K, et al. Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms. Eur Heart J-Digit Health, 2021, 2(3): 401-415..
38. Sivakumaran S, Krahn A D, Klein G J, et al. A prospective randomized comparison of loop recorders versus Holter monitors in patients with syncope or presyncope. Am J Med, 2003, 115(1): 1-5..
39. Maron D J, Hochman J S, O'Brien S M, et al. International study of comparative health effectiveness with medical and invasive approaches (ISCHEMIA) trial: Rationale and design. Am Heart J, 2018, 201: 124-135..

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