睡眠PPG-Net2:深度学习的概括,用于睡眠阶段测定从光电缩图.
Shirel Attia1,2, Revital Shani Hershkovich3, Alissa Tabakhov3
1Faculty of Data and Decision Sciences, Technion, Israel Institute of Technology, Haifa, Israel.
Physiological measurement
|October 31, 2025
概括
这项研究介绍了SleepPPG-Net2,这是一种深度学习模型,可以从光聚光显微镜 (PPG) 数据中改善睡眠阶段. 它增强了跨不同数据集的概括性,解决了睡眠健康诊断中的数据漂移挑战.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 睡眠医学 睡眠医学
背景情况:
- 睡眠分期对于诊断睡眠障碍和管理睡眠健康至关重要.
- 手动的睡眠评分是耗时的.
- 目前的基于光聚光学 (PPG) 的深度学习模型因数据漂移而面临泛化挑战.
研究的目的:
- 通过使用原始PPG时间序列,评估多源域培训,以改善四类睡眠阶段的分布外概括.
- 开发和评估一个名为SleepPPG-Net2.2的深度学习模型.
- 调查人口因素,种族和阻塞性睡眠呼吸暂停 (OSA) 对模型性能的影响.
主要方法:
- 开发了SleepPPG-Net2,这是一个深度学习模型,利用多源域培训来进行睡眠分阶段.
- 与两种最先进的模型进行比较,对 SleepPPG-Net2 进行了比较.
- 分析了与年龄,性别,种族和OSA严重程度相关的性能变化.
主要成果:
- 与基准模型相比,SleepPPG-Net2表现优越,在概括方面提高了21% (科恩的卡帕).
- 观察到与人口因素 (年龄,性别) 和OSA严重程度相关的显著绩效差异.
结论:
- 睡眠PPG-Net2显著提高了基于PPG的睡眠阶段的准确性和概括性.
- 该研究强调了人口和临床因素对人工智能模型在睡眠分析中的表现的影响.
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