IEEE J Biomed Health Inform. 2017 Nov;21(6):1546-1553. doi: 10.1109/JBHI.2017.2650199. Epub 2017 Jan 9.
Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this. The first approach used EEG data from the whole sleep recording irrespective of the sleep stage (stage-independent classification), while the second used only EEG data from insomnia-impacted specific sleep stages (stage-dependent classification). We trained and tested our system using both healthy and disordered sleep collected from 41 controls and 42 primary insomnia patients. When compared with manual assessments, an NREM + REM based classifier had an overall discrimination accuracy of 92% and 86% between two groups using both two and one EEG channels, respectively. These results demonstrate that deep learning can be used to assist in the diagnosis of sleep disorders such as insomnia.
有效的睡眠分析受到缺乏针对睡眠紊乱模式的自动化工具和繁琐的监测硬件的阻碍。在本文中，我们应用深度学习对从最多两个脑电图通道提取的 57 个脑电图特征集进行分析，以准确区分失眠患者和无睡眠抱怨的对照组。我们研究了两种不同的方法来实现这一目标。第一种方法使用整个睡眠记录中的脑电图数据，而不管睡眠阶段如何（与阶段无关的分类），而第二种方法仅使用受失眠影响的特定睡眠阶段的脑电图数据（与阶段相关的分类）。我们使用来自 41 名健康对照者和 42 名原发性失眠患者的健康和紊乱睡眠数据来训练和测试我们的系统。与手动评估相比，基于 NREM+REM 的分类器在分别使用两个和一个脑电图通道时，在两组之间的总体判别准确率分别为 92%和 86%。这些结果表明，深度学习可用于辅助诊断失眠等睡眠障碍。
