机器学习方法用于预测基于心率变化,氧和和身体形状的睡眠唤醒反应
Chih-Fan Kuo1,2,3, Cheng-Yu Tsai4,5, Wun-Hao Cheng6,7
1School of Medicine, China Medical University, Taichung City, Taichung, Taiwan.
Digital health
|October 17, 2023
概括
这项研究使用心率变化和氧和来预测睡眠唤醒,这是阻塞性睡眠呼吸暂停查的关键. 机器学习模型确定了特定的心率模式作为兴奋的关键预测因素.
科学领域:
- 睡眠医学 睡眠医学
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一个广泛的健康问题.
- 当前的查工具往往忽视睡眠唤醒,影响睡眠效率.
- 需要新的方法来评估与呼吸事件一起的兴奋.
研究的目的:
- 开发一个用于睡眠唤醒发生的预测模型.
- 使用易于测量的参数,如心率变化 (HRV) 和氧和 (SpO2).
- 为了比较不同机器学习方法对兴奋预测的有效性.
主要方法:
- 收集了来自659名患者的身体形状和多睡眠学数据.
- 测量了连续HRV和SpO2,标记了唤醒存在的数据.
- 利用身体形状,HRV和SpO2变量的数据集进行训练和测试机器学习模型.
主要成果:
- 开始时间模型在预测睡眠唤醒方面表现出卓越的表现.
- 在测试数据集中获得了76.21%的准确性,84.33%的AUC-ROC和86.28%的AUC-PR.
- 确定了R-R间隔的标准偏差和连续的正常心跳作为关键预测指标.
结论:
- 开发的模型显示了选睡眠唤醒的前景.
- 这些模型可以集成到可穿戴设备中,用于在家监测睡眠.
- 这种方法提供了对OSA睡眠障碍的更全面的评估.
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