对感知睡眠质量的个性化可解释预测:具有有意义的心血管和行为特征的模型
1Department of Computer Science, ETH Zurich, Zürich, Switzerland.
PloS one
|July 8, 2024
概括
这项研究将可穿戴设备数据与感知睡眠质量联系起来. 清醒时心率较高和睡眠时神经系统活动较低与更好的睡眠质量相关.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠科学 睡眠科学
- 数据科学数据科学数据科学
背景情况:
- 感知睡眠质量对健康至关重要,但由于个体差异,很难衡量.
- 睡眠质量会影响日常认知功能,疲劳以及长期的身体和精神健康.
- 很少有研究使用生物信号在多天内模拟感知睡眠质量.
研究的目的:
- 用可解释的模型研究心脏活动和感知睡眠质量之间的关联.
- 利用商品可穿戴设备的数据来评估睡眠质量.
- 为医学上有洞察力的生物信号分析提出每个参与者的正常化.
主要方法:
- 使用了M2Sleep数据集,这是一个密集的纵向研究.
- 采用可解释的建模,使用来自可穿戴运动和血液体积脉冲传感器的输入信号.
- 应用每个参与者的规范化和可解释的AI技术.
主要成果:
- 在预测感知睡眠质量方面达到高达70%的准确性和0.76 AUC.
- 与现有方法相比,预测误差降低了高达36%.
- 确定了关键预测因素:皮肤温度升高,睡眠时间足够长,清醒时心率升高,以及睡眠期间神经系统的副交感/交感活动减少.
结论:
- 商品可穿戴生物信号可以有效地模拟感知睡眠质量.
- 每个参与者的正常化对于有意义地分析睡眠质量数据至关重要.
- 可解释的模型显示,在清醒和睡眠期间的生理标记显著影响感知到的睡眠质量.
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