在中使用标准脑电图和可穿戴数据的深度学习进行自动化睡眠分期
Jaiver Macea1, Elisabeth R M Heremans2, Renee Proost3,4
1Laboratory for Epilepsy Research, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
Journal of sleep research
|April 3, 2025
概括
使用可穿戴设备的自动睡眠分期显示了患者的中度准确性. 虽然对睡眠监测有希望,但对临床使用需要进一步改进.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
背景情况:
- 使用可穿戴设备的自动睡眠分期为改善管理提供了潜力.
- 目前的方法通常需要繁的临床设备,如脑电图 (EEG).
研究的目的:
- 评估深度学习模型在自动睡眠分期中的表现,使用可穿戴数据与标准EEG相比.
- 探索发作和没有发作的患者的睡眠结构差异.
主要方法:
- 在50名患者的223个夜间睡眠记录中使用深度学习模型进行睡眠分阶段.
- 用医院的EEG和可穿戴设备收集数据.
- 模型性能与临床专家评分使用布兰德-阿尔特曼分析和混合效应模型进行了比较.
主要成果:
- 该模型与临床专家相比,实现了中度的准确性 (科恩卡帕0.59对EEG,0.43对可穿戴设备).
- 可穿戴数据低估了大多数睡眠宏观结构参数,除了N2睡眠.
- 与没有发作的患者相比,患者睡得更久,在N2睡眠中花费的时间也更长.
结论:
- 可穿戴式EEG和加速度计显示了对患者睡眠监测的潜力.
- 自动化分析方法需要进一步细化,以便在临床实施.
- 睡眠监测可能会揭示与活动相关的睡眠模式的差异.
相关概念视频
01:22Stages of Sleep
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Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...
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01:07Narcolepsy
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Narcolepsy is a chronic sleep disorder characterized by pervasive, uncontrolled sleepiness and other sleep disturbances. One of its hallmark symptoms is an abrupt transition to REM sleep upon falling asleep, which causes symptoms typically associated with this phase to occur unexpectedly during wakefulness. These include the following symptoms, which typically last from a minute or two to half an hour.
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