使用深度学习进行临时连续自动化睡眠-觉醒分类
Pranavan Somaskandhan1, Henri Korkalainen2,3, Timo Leppänen1,2,3
1School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia.
medRxiv : the preprint server for health sciences
|December 17, 2025
概括
这项研究引入了一个深度学习的睡眠-清醒分类器,可以克服固定30秒时代的局限性. 这种新型模型提供了高时间分辨率的睡眠评分,以获得更准确的生理评估.
科学领域:
- 睡眠科学 睡眠科学
- 计算神经科学是一种计算神经科学.
- 医学中的人工智能
背景情况:
- 目前的睡眠评分依赖于固定的30秒时段,这些时段可能不能准确地代表睡眠动态.
- 这种限制可能会阻碍精确的生理睡眠评估.
研究的目的:
- 开发一种基于深度学习的睡眠和清醒分类器,具有高时间分辨率.
- 利用时间连续的手动评分,绕过固定的时代界限.
- 为了提高睡眠评估的生理一致性.
主要方法:
- 基于U-Net的深度学习模型被训练在睡眠和清醒数据上.
- 使用转移学习,微调模型以时间连续的得分数据.
- 该模型在使用连续评分的独立数据集上进行了验证.
主要成果:
- 通过连续的手动评分,分类器实现了高一致性 (88.96%和88.23%).
- 在总睡眠时间 (r=0.93) 和睡眠到觉醒过渡 (r=0.67) 的1秒预测和手动评分之间观察到强烈的相关性.
结论:
- 开发的模型有效地解决了传统的30秒时代评分的局限性.
- 这种方法为更具生理一致性的睡眠和清醒评估提供了实际基础.
- 预测信心估计可以指导针对性审查潜在的错误分类.
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