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Multi-Modal Home Sleep Monitoring in Older Adults
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通过可穿戴反射光电显微镜实现长期睡眠分期
Loris Constantin1, Christian M Horvath2, Florent Baty3
1Swiss Center of Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland.
Sleep
|August 21, 2025
概括
一个基于智能手表的深度学习模型准确地使用光电显微镜 (PPG) 测定睡眠阶段,为门诊睡眠监测提供了可扩展的多睡眠显微镜 (PSG) 替代方案.
科学领域:
- 生物医学工程
- 睡眠科学
- 医疗保健中的人工智能
背景情况:
- 多睡眠法 (PSG) 是衡量睡眠阶段的黄金标准, 但它昂贵,耗时且难以扩展.
- 可穿戴式光电显微镜 (PPG) 为睡眠监测提供了一个潜在的非侵入性,可访问的替代方案.
研究的目的:
- 评估使用智能手表记录的PPG进行自动睡眠分阶段的深度学习模型.
- 在夜间和长期的门诊记录中评估模型的概括性和性能.
主要方法:
- 一个深度学习模型,先前训练了通用性,在两个手腕佩戴PPG记录的数据集上进行了测试.
- 与专家评分的PSG睡眠阶段进行了比较.
- 长期记录 (n=493,持续时间为24小时) 与动图相比较.
主要成果:
- 对于夜间记录,PPG模型实现了78.7%的准确性 (科恩 κ=0.68) 对于4级睡眠阶段和94.1%的准确性 (科恩 κ=0.71) 对于对PSG的睡眠/清醒分类.
- 在长期记录中,与动图相比,该模型达到92.5%的睡眠/清醒精度 (科恩的 κ=0.80).
结论:
- 基于PPG的模型在手腕设备上展示了最先进的准确性,为睡眠分期提供了可行的PSG替代方案.
- 长期监测的有希望的结果表明可能存在连续的门诊睡眠阶段和睡眠-清醒周期跟踪.
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