人工智能和机器学习在可穿戴心电图中的演变作用:可穿戴可启用预测心脏功能障碍的入门教程
Aditya Dave1, Amartya Dave2, Issam D Moussa1
1Carle Heart and Vascular Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Bioengineering (Basel, Switzerland)
|February 27, 2026
概括
人工智能和机器学习正在彻底改变可穿戴心电图 (ECG) 对心脏病的监测. 本综述综合了当前的AI/ML应用,并突出了提高这种新兴技术可靠性的领域.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 可穿戴式心电图 (ECG) 设备越来越普遍.
- 人工智能 (AI) 和机器学习 (ML) 正在推动医疗信号处理.
- 可穿戴技术为心脏病如缺血和心律失常提供了个性化的远程监测.
研究的目的:
- 审查AI和ML在可穿戴心电图技术中的当前应用.
- 为了合成主要心脏病状况的场状.
- 确定局限性,并建议改进以实现广泛采用.
主要方法:
- 对AI/ML应用在可穿戴心电图中的当前文献的综述.
- 对外围心电图数据的AI/ML技术的综合.
- 对各种心脏病的用例分析.
主要成果:
- 使用可穿戴心电图数据的AI/ML研究显著增长.
- 识别用于构建来自门诊心电图模型的实用工具.
- 对可穿戴心电图的AI/ML当前能力和局限性的总结.
结论:
- 人工智能和机器学习具有可穿戴心脏监测的巨大潜力.
- 需要进一步的研究来提高这些技术的可靠性和临床实用性.
- 本综述旨在指导研究人员和开发人员在可穿戴心电图中推进AI/ML.
关键词:
电心电图是指心电图.人工智能的人工智能是人工智能.心房动是心房动的一种.深度学习是一种深度学习.心脏衰竭是因为心脏衰竭.缺血缺血症是因为缺血.机器学习是机器学习.心肌梗塞的心脏病发作信号处理 信号处理 信号处理穿戴式技术是一种可穿戴技术.更多相关视频
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