通过时间上下文优化增强基于光电吸收的睡眠分期模型
Joseph A P Quino1, Diego A C Cardenas1, Marcelo A F Toledo1
1Heart Institute, University of Sao Paulo (INCOR), Sao Paulo, SP, Brazil.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究引入了一种新的睡眠分期模型,使用较短的光电显微镜 (PPG) 信号. 该方法通过结合15分钟间隔的上下文信息来提高可穿戴设备的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 信号处理 信号处理
背景情况:
- 准确的睡眠阶段分类对于诊断睡眠障碍和评估睡眠质量至关重要.
- 聚睡眠学 (PSG) 是黄金标准,但光聚体学 (PPG) 为可穿戴设备提供了切实可行的替代方案.
- 目前的方法通常需要长时间的信号采集,由于能源限制,限制了可穿戴的可行性.
研究的目的:
- 开发一个适应睡眠阶段模型,优化用于较短的PPG信号段.
- 为了平衡可穿戴睡眠监测应用程序的精度和能源效率.
- 为了提高睡眠分期性能,使用来自连接的PPG段的上下文信息.
主要方法:
- 根据最先进的睡眠分期模型进行了调整.
- 在15分钟间隔内连接30秒的PPG片段,以捕捉扩展的背景.
- 使用准确度,科恩的卡帕和F1加权得分来评估模型的表现.
主要成果:
- 获得了0.75的准确性,科恩的卡帕为0.60,F1加权得分为0.74.
- 提出的方法始终优于仅使用短时间PPG段的模型.
- 通过上下文意识的方法,改善了睡眠分期的准确性.
结论:
- 使用连接PPG段的上下文意识方法可以提高能量受限制的可穿戴设备中的睡眠分期精度.
- 拟议的方法为实际的可穿戴睡眠监测提供了一个可行的解决方案.
- 进一步的研究可以探索优化细分连接以提高性能.
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