PSNSleep:一种基于罗网络的自主监督学习方法,用于睡眠分阶段,只有积极的样本对
Yuyang You1, Shuohua Chang1, Zhihong Yang2
1School of Automation, Beijing Institute of Technology, Beijing, China.
Frontiers in neuroscience
|June 22, 2023
概括
一种新的自主监督学习方法PSNSleep有效地使用语网络和时间移动数据增强来分阶段睡眠. 这种方法可以实现高精度,不需要负样本,简化睡眠数据分析.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
背景情况:
- 睡眠分阶段的监督学习需要广泛的,专家标记的多睡眠学数据,这是耗时和昂贵的.
- 自主监督学习 (SSL) 提供了一个有前途的替代方案,通过减少对标记数据的依赖来提取特征.
- 现有的SSL方法通常严重依赖于负样本构造,这对睡眠分期应用程序构成挑战.
研究的目的:
- 介绍PSNSleep,一种用于自动化睡眠分期的新型自我监督学习方法.
- 通过使用精心选择的数据增强,消除了在基于SSL的睡眠分期中对负样本的需求.
- 为了证明PSNSleep在公共睡眠数据集上的有效性.
主要方法:
- 开发了PSNSleep,这是一个基于语网络的自主监督学习框架,专为睡眠阶段定制.
- 采用时移数据增强策略来创建培训的积极样本对.
- 在不使用任何负样本的情况下,对睡眠EDF和ISRUC睡眠数据集的方法进行了验证.
主要成果:
- 在睡眠-EDF数据集上,PSNSleep 实现了 80.0% 的准确性.
- 该方法在ISRUC-Sleep数据集上获得了74.4%的准确性.
- 在不依赖负样本采矿的情况下获得了令人满意的性能.
结论:
- PSNSleep 提供了一种有效的自我监督的睡眠分期方法,减少了数据标签的负担.
- 拟议的时移增强有效地构建了正样本对,使得没有负样本的高性能.
- 源代码的公开可用性促进了自动化睡眠分析的进一步研究和应用.
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