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[Artificial intelligence in wearable electrocardiogram monitoring].

作者信息

Wang Xingyao, Li Qian, Ma Caiyun, Zhang Shuo, Lin Yujie, Li Jianqing, Liu Chengyu

机构信息

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.

State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Dec 25;40(6):1084-1092. doi: 10.7507/1001-5515.202301032.


DOI:10.7507/1001-5515.202301032
PMID:38151930
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10753313/
Abstract

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.

摘要

相似文献

[1]
[Artificial intelligence in wearable electrocardiogram monitoring].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023-12-25

[2]
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[3]
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[7]
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[8]
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[10]
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本文引用的文献

[1]
Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms.

Eur Heart J Digit Health. 2021-5-8

[2]
Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Prediction: A Report From the Huawei Heart Study.

JACC Asia. 2021-12-21

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Adversarial attacks and adversarial robustness in computational pathology.

Nat Commun. 2022-9-29

[4]
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IEEE Trans Pattern Anal Mach Intell. 2023-4

[5]
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Sci Adv. 2022-5-27

[6]
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J Med Internet Res. 2021-11-9

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Eur Heart J. 2021-12-7

[8]
Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease.

NPJ Digit Med. 2020-7-9

[9]
Deep learning models for electrocardiograms are susceptible to adversarial attack.

Nat Med. 2020-3-9

[10]
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Sci Data. 2020-2-12

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