人工智能驱动的睡眠分期使用瞬间心率和加速度计:来自Apple Watch研究的见解
IEEE transactions on bio-medical engineering
|September 19, 2025
概括
这项研究引入了一个使用智能手表数据进行自动睡眠分阶段的AI框架,达到71%的准确性. 这为睡眠评估提供了一个可扩展的替代传统的多睡眠学.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 睡眠科学 睡眠科学
背景情况:
- 多睡眠学 (PSG) 是睡眠评估的黄金标准,但复杂且劳动密集.
- 消费者可穿戴设备为睡眠监测提供了一个可扩展,可访问的替代方案.
- 使用可穿戴数据进行自动睡眠分期需要强大的AI框架.
研究的目的:
- 使用智能手表数据开发和验证人工智能驱动的自动化睡眠分期框架.
- 为了比较基于智能手表的睡眠阶段与电脑电图 (EEG) 参考标准.
- 确定用于增强睡眠分阶段性能的关键架构组件.
主要方法:
- 一项涉及47名健康成年人使用Apple Watch Series 6和Dreem 2头带的多夜研究.
- 开发一个具有卷积层的序列对序列长短期记忆 (LSTM) 模型.
- 利用瞬间心率 (IHR) 和加速度计数据,以基于EEG的睡眠阶段作为基本真相.
主要成果:
- 人工智能框架在将睡眠时代分为清醒,轻度,深度和REM睡眠时达到71%的准确性.
- 废除研究确定了时代内学习LSTM,IHR采样频率和跳过连接对于性能至关重要.
- 开发了一个用于数据采集的Apple Watch应用程序,一个开源库和一个公共数据集.
结论:
- 使用智能手表数据进行人工智能驱动的睡眠分期是PSG的可行和可扩展的替代方案.
- 开发的框架显示了常规和长期睡眠监测的巨大潜力.
- 开源图书馆和公共数据集有助于未来的自动化睡眠分析研究.
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