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普通睡眠深度:以数据驱动的持续测量睡眠深度
Erik-Jan Meulenbrugge1,2, Haoqi Sun1,2, Wolfgang Ganglberger1,2
1Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Journal of sleep research
|April 25, 2025
概括
一种新的深度学习模型,即顺序睡眠深度 (OSD),提供持续测量睡眠深度. OSD与唤醒概率有很强的相关性,并准确地反映了因年龄,性别,睡眠呼吸障碍和认知障碍而导致的变化.
科学领域:
- 神经科学是一个神经科学.
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
背景情况:
- 传统的睡眠分阶段使用离散的阶段,可能错过了睡眠深度的连续性.
- 需要一种更细微,数据驱动的睡眠深度测量方法.
研究的目的:
- 开发和验证一个持续测量睡眠深度,普通睡眠深度 (OSD),使用深度学习.
- 评估OSD与唤醒概率的相关性及其与年龄,性别,睡眠呼吸障碍 (SDB) 和认知障碍的关联.
主要方法:
- 一个卷积神经网络被训练在21,787个多睡眠记录,以估计持续的睡眠深度.
- 顺序回归被纳入,以考虑非REM睡眠阶段的有序性质.
- OSD与几率比率产物 (ORP) 进行比较,并与临床变量相关联.
主要成果:
- OSD与兴奋概率 (r=0.994) 呈现出强烈的线性相关性,其表现优于ORP (r=0.923).
- OSD反映了与年龄相关的睡眠深度下降,并显示女性的睡眠深度比男性更深.
- 在SDB和认知障碍患者中,OSD更准确地捕获了睡眠深度的减少.
结论:
- 普通睡眠深度 (OSD) 是一个经过验证的,数据驱动的睡眠深度测量方法,与唤醒概率有很强的相关性.
- OSD有效地捕捉了与年龄,性别,SDB和认知障碍相关的睡眠深度变化.
- OSD提供了对睡眠架构的细微理解,强调睡眠深度作为一个关键维度.
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