使用数字生物标志物和人工神经网络预测睡眠质量
Hyolim Lee1, Minsung Cho2, Sang Won Lee3,4
1Department of Artificial Intelligence Convergence, Kangwon National University, Gangwon, Republic of Korea.
Frontiers in psychiatry
|July 31, 2025
概括
穿戴式心率变化 (HRV) 监测可以预测第二天的睡眠质量. 低频与高频 (LF/HF) 的比率是评估睡眠碎片化的关键数字生物标志物,能够主动管理睡眠健康.
科学领域:
- 生物医学工程 生物医学工程
- 数字健康数字健康
- 睡眠科学 睡眠科学
背景情况:
- 越来越多的压力和不规则的生活方式增加了失眠的患病率,需要有效的睡眠质量评估.
- 可穿戴设备为健康监测提供持续的生理数据收集.
- 觉醒后睡眠开始 (WASO) 是评估睡眠碎片化的关键指标.
研究的目的:
- 为了研究可穿戴设备和睡眠质量之间心率变化 (HRV) 的关系.
- 使用数字生物标志物开发预测模型来预测第二天的睡眠质量.
- 为了确定预测睡眠碎片化的关键HRV特征.
主要方法:
- 收集了82名使用Samsung Galaxy Watch Active 2的参与者的生物识别数据 (HRV,步骤) 和主观睡眠质量数据.
- 分析了前七天的数据,用机器学习模型 (ARIMA,随机森林,XGBoost,GRU,TCN,变压器,LSTM) 来预测第二天的WASO.
- 利用LIME分析来确定模型预测的特征重要性.
主要成果:
- 低频与高频 (LF/HF) 的HRV比率是与WASO最强的相关 (p=0.012).
- 对于二进制WASO分类,LSTM模型实现了90.4%的准确性,91.3%的精度和89.9%的回忆.
- 确定LF/HF比率,ISI和WHOQOL-BREF得分是最有影响力的预测因素.
结论:
- 通过可穿戴设备的持续HRV监测为睡眠健康管理提供了一种新的方法.
- 这种LF/HF比率作为一个有前途的数字生物标志物用于预测睡眠质量和碎片化.
- 深度学习模型擅长识别时间模式,以进行主动,个性化的睡眠健康干预.
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