深度学习算法的验证,用于在门诊环境中使用手腕穿戴设备持续实时检测心房动
Ming-Zher Poh1, Anthony J Battisti2, Li-Fang Cheng1
1Verily Life Sciences South San Francisco CA.
Journal of the American Heart Association
|September 26, 2023
概括
一种新的手腕穿戴设备在检测心房动 (AF) 和估计其负担时表现出高准确度,使用连续光电脉学. 这种可穿戴技术为长期AF监测和管理提供临床级性能.
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:
- 消费者可穿戴设备提供周期性心房动 (AF) 检测,但缺乏临床级,持续监测能力.
- 目前的设备被清除用于预诊断用途,而不是临床决策,突出了对医疗级解决方案的未满足需求.
- 长期,持续的AF监测对于诊断和管理至关重要.
研究的目的:
- 为了评估一个带有心电图和光斑显微镜 (PPG) 的手腕穿戴设备的性能,用于AF检测和负载估计.
- 评估基于PPG的卷积神经网络 (CNN) 算法用于AF监测的准确性.
- 将设备的性能与参考心电图监视器进行比较.
主要方法:
- 一项前性多中心研究招募了117名患有偏发性AF的患者.
- 用于连续监测,使用带头I心电图和PPG的手腕设备 (Verily Study Watch).
- 一个持续14天的心电图监测器 (Zio XT) 作为算法评估的参考标准.
主要成果:
- 腕式设备在15分钟间隔内实现了96.1%的灵敏度和98.1%的特异性,用于15分钟间隔的AF检测.
- 从PPG推导的AF负荷估计显示与参考设备的高相关性 (R2=0.986).
- 在111名具有可评估数据的受试者中,佩戴手表的平均时间为18.3小时/天.
结论:
- 基于PPG的CNN在手腕穿戴设备中提供临床级的AF检测和负担估计.
- 使用这种可穿戴技术的持续监测支持AF诊断和临床管理.
- 这一进步解决了对可靠,长期AF监控解决方案的需求.
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