AnySleep:一种通道不可分的深度学习系统,用于在多中心队列中高分辨率的睡眠阶段
ArXiv
|December 25, 2025
概括
手动的睡眠评分是耗时的. 深度神经网络AnySleep使用电脑图 (EEG) 或电眼图 (EOG) 数据在各种分辨率上准确地得分睡眠,改善生物标志物发现.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
背景情况:
- 从多睡眠学 (PSG) 手动睡眠阶段是劳动密集型,并对多中心研究提出了挑战.
- 传统的30秒时代评分缺乏生理基础,并且限制了在更短的时间尺度上发现生物标志物.
- 电极数量和安装在不同中心的变化使协调睡眠研究变得复杂.
研究的目的:
- 开发一个深度神经网络模型 (AnySleep) 用于使用多种电脑图 (EEG) 和电眼图 (EOG) 数据进行自动睡眠评分.
- 为了在可调的时间分辨率上实现睡眠评分,包括30秒以下的间隔.
- 为了促进在多个临床场所和异质数据采集设置中进行强有力的概括.
主要方法:
- 在21个不同数据集的19,000多个夜间记录上训练和验证了AnySleep,总计近20万小时的EEG和EOG数据.
- 对30秒时段和30秒以下时间尺度的既定基线进行模型性能评估.
- 评估的模型能够在不同数量的频道上运行,包括有限或不存在的EOG和单个EEG衍生.
主要成果:
- AnySleep实现了最先进的性能,在30秒的时代与传统方法相匹配或超过.
- 模型的性能即使在EOG或单个EEG频道的最小或缺席的情况下也保持强.
- 在小于30秒的时间尺度上,AnySleep准确地识别了短时间的入侵 (兴奋),并改善了对生理和病理生理条件的预测.
结论:
- AnySleep提供了一种灵活而强大的解决方案,用于使用随时可用的EEG/EOG数据进行自动睡眠评分.
- 该模型能够处理数据异质性和可变时间分辨率,这有助于大规模的多中心睡眠研究.
- 预计AnySleep的公共可用性将加速发现新的睡眠生物标志物,并改善临床睡眠分析.
相关概念视频
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