使用最小的非EEG生理信号集和深度学习优化睡眠阶段检测
Ángel Serrano Alarcón1,2, Maksym Gaiduk3, Natividad Martínez Madrid1
1IoT Laboratory, Reutlingen University, Reutlingen, Baden-Württemberg, Germany.
Journal of sleep research
|December 14, 2025
概括
这项研究引入了一个可重现的深度学习框架,用于自动分类睡眠阶段,仅使用氧和度,心率和呼吸力度. 这种无EEG的方法使得可扩展的,非侵入性的,在家进行睡眠监测.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠科学 睡眠科学
- 人工智能的人工智能
背景情况:
- 自动睡眠阶段分类对于家庭监测至关重要,但通常依赖于电脑电图 (EEG) 信号,限制可重现性.
- 目前使用EEG的方法复杂,阻碍了广泛的,非侵入性的应用.
研究的目的:
- 开发一个可重现的工程框架,用于深度学习模型的睡眠阶段分类.
- 为了使精确的睡眠监测使用易于获得的生理信号,避免EEG.
主要方法:
- 开发了一个基于U-Net的深度学习模型,用于将睡眠分为五个或四个阶段.
- 该模型利用氧和度 (SpO2),心率 (HR) 和腹部呼吸力 (AbdRes),根据每秒的时间进行预测.
- 在SHHS2数据集上使用Keras Tuner与Hyperband算法进行模型优化,并在MESA数据集上验证.
主要成果:
- 该模型在SHHS2数据集上实现了68% (五阶段) 和71% (四阶段) 的加权F1分数,科恩的卡帕分别为0.61和0.67.
- 在MESA数据集的外部验证过程中观察到一致的性能.
- 该研究表明,开发的模型具有强大的概括能力.
结论:
- 这种轻量级,无EEG的方法为临床相关的睡眠监测提供了实用和可扩展的解决方案.
- 该框架推进了在家进行的非侵入性睡眠分析,克服了传统方法的局限性.
- 每秒的预测能力增强了睡眠阶段分类的细粒度.
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