使用智能手表对心房动负担进行算法引导的光电显微镜的评估
Zixu Zhao1, Qifan Li1, Sitong Li1
1Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University and National Clinical Research Center for Cardiovascular Diseases, Beijing, China.
Pacing and clinical electrophysiology : PACE
|February 26, 2024
概括
智能手表使用光电感应 (PPG) 算法准确监测心房动 (AF) 负担. 这种可穿戴技术显示出持续诊断和管理AF的潜力.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数字健康数字健康
背景情况:
- 使用光电感应 (PPG) 算法的可穿戴设备在检测心房动 (AF) 中表现出有效性.
- 需要进一步的研究来评估PPG对AF负担的长期,持续监控能力.
研究的目的:
- 为了评估智能手表的性能,使用连续的PPG和基于PPG的算法来估计AF负担.
- 根据参考标准验证这种可穿戴技术的准确性和稳定性.
主要方法:
- 一项前性研究招募了245名接受导管切除的AF患者.
- 来自智能手表的连续PPG数据与连续心电图 (ECG) 补丁在578,669个30秒间隔内进行了比较.
- 对AF检测和负载估计进行了算法性能评估.
主要成果:
- 在AF负担估计方面,PPG实现了高间隔级别的灵敏度 (96.3%) 和特异性 (99.5%).
- 基于PPG的AF负担估计与ECG有很强的相关性 (R2 = 0.996),平均差异很小 (-0.59%).
- 该算法在各种子组中展示了强大的性能,包括不同的心率和一天中的不同时间.
结论:
- 基于PPG算法的智能手表提供了准确和稳定的AF负载持续监控.
- 这项技术在诊断和治疗心房动方面具有显著的潜力.
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