在现实世界中设置中,从带有神经网络的可穿戴设备中计算缺失的睡眠数据
Minki P Lee1, Kien Hoang2, Sungkyu Park3
1Department of Mathematics, University of Michigan, Ann Arbor, MI, USA.
Sleep
|October 11, 2023
概括
我们开发了SOMNI,一种使用非负矩阵分解 (NMF) 的机器学习模型,以准确地填补来自动图的缺失睡眠数据. 这有助于监测临床环境之外的患者的不规则睡眠模式.
科学领域:
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
- 睡眠医学 睡眠医学
背景情况:
- 准确的纵向睡眠数据对健康至关重要,但在实验室之外收集这些数据具有挑战性.
- 动图提供休息活动数据,但往往缺少值,阻碍了分析.
- 睡眠模式不规则的个体对睡眠监测提出了独特的挑战.
研究的目的:
- 引入SOMNI (使用机器学习和非负矩阵因子化的睡眠数据恢复),这是一个新的神经网络模型,用于归纳缺失的动画图数据.
- 评估SOMNI在处理失踪睡眠数据方面的个人和全球方法的表现,特别是对于睡眠和清醒周期受损的个人.
- 为临床医生提供一个工具,以便更好地管理缺失的睡眠数据,并监测睡眠模式不规则的患者.
主要方法:
- 开发了一种包含非负矩阵因子化 (NMF) 的两层隐藏神经网络模型.
- 实施了两个数据归算方法:个人 (单参与者数据) 和全球 (多参与者数据).
- 在三家医院使用轮班和非轮班工人的休息活动数据验证了SOMNI模型.
主要成果:
- 个人和全球SOMNI方法都准确地归算了长时间 (>50天) 的数据集的缺失数据,即使是轮班工人 (AUC > 0.86).
- 对于较短的数据集 (约15天),只有全局方法表现出准确性 (AUC > 0.77).
- 该模型有效地捕捉了睡眠障碍患者隐藏的纵向睡眠-清醒模式.
结论:
- 索姆尼 (SOMNI) 提供了一个准确的方法来归因失踪的动图数据,提高了睡眠和清醒周期的监测.
- 该模型对于长期监测和高度不规则的睡眠模式的个体尤其有效.
- 这种工具可以通过在实验室外进行可靠的数据分析来改善睡眠障碍的临床管理.
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