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Multi-Modal Home Sleep Monitoring in Older Adults
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一种深度转移学习方法用于睡眠阶段分类和睡眠呼吸暂停检测,使用手腕穿戴的消费者睡眠技术
IEEE transactions on bio-medical engineering
|March 18, 2024
概括
消费者睡眠技术可以帮助选阻塞性睡眠呼吸暂停 (OSA). 一个使用手腕戴设备数据的深度学习模型显示了检测这种常见睡眠障碍的前景.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种普遍但经常未被诊断的睡眠障碍.
- OSA对整体健康和福祉构成重大风险.
- 当前的诊断方法可能是资源密集的.
研究的目的:
- 开发和验证使用消费者睡眠技术 (CST) 检测睡眠呼吸暂停的深度转移学习模型.
- 评估使用加速仪和手腕穿戴设备的光电显微图数据用于睡眠呼吸暂停查的有效性.
- 评估模型在不同数据源 (临床与CST) 的性能.
主要方法:
- 一个深度卷积神经网络 (DNN) 用于睡眠阶段分类和睡眠呼吸暂停检测.
- 该DNN使用夜间记录的原始加速度计和光电显微镜信号.
- 模型培训和内部测试使用了临床和CST数据集,在单独的CST数据集上进行外部验证.
主要成果:
- 对临床数据的培训显著提高了模型性能.
- 在使用CST数据集时,直接原始数据输入的性能优于基于特征的输入.
- 该模型表现出良好的概括性,与CST数据相比,临床数据的性能略有改善.
- 该系统在REM睡眠期间检测事件方面表现特别强,与兴奋和氧气脱度相关.
结论:
- 消费者睡眠技术有可能成为一种可行的查工具,用于识别未被诊断的阻塞性睡眠呼吸暂停病例.
- 开发的深度转移学习方法为改善各种可穿戴设备的睡眠呼吸暂停检测提供了一个有希望的方法.
- 进一步验证和整合CST可以扩大OSA查的可访问性.
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