Automated Sleep Staging on Wearable EEG Enables Sleep Analysis at Scale

Maurice Abou Jaoude, Aravind Ravi, Jiansheng Niu, Hubert J. Banville, Nicolas Florez Torres, Christopher Aimone
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Abstract

This study presents automated sleep staging on a large number of sleep electroencephalography (EEG) recordings collected using the Muse S headband. Two recent deep learning models; a single-channel Deep Sleep Net (DSN) and a multi-channel Muse Net (MNet) were evaluated on a 5-class sleep stage classification task on 200 expert-labelled overnight sleep EEG recordings. The learned representations of the models were visualized using uniform manifold approximation projection (UMAP). Moreover, a large scale analysis of the relationship between sleep stage distribution of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep with age was performed on 1020 unlabelled EEG recordings. The results showed that the proposed models achieved high accuracy (DSN: 85.2%, MNet: 86.3%) and Cohen's Kappa (DSN: 0.77, MNet: 0.79) indicating substantial agreement with human expert sleep scoring. Furthermore, the features learned by the deep neural networks showed a sleep continuum beyond the traditionally used sleep stages. Hypnogram analysis revealed a decrease in percentage of NREM 3 and REM sleep with increasing age, and an increase in percentage of NREM 2 sleep with increasing age. The results suggested that a 4-channel wearable EEG headband provides low-cost and powerful means to automatically score and analyze sleep at a large scale.
可穿戴脑电图的自动睡眠分期实现大规模睡眠分析
本研究采用Muse S头带收集的大量睡眠脑电图(EEG)记录进行自动睡眠分期。两个最近的深度学习模型;对200个专家标记的夜间睡眠脑电图记录进行5级睡眠阶段分类任务,对单通道深度睡眠网(DSN)和多通道Muse网(MNet)进行评估。使用均匀流形近似投影(UMAP)将模型的学习表征可视化。此外,对1020例无标记脑电图记录进行了非快速眼动(NREM)和快速眼动(REM)睡眠阶段分布与年龄的关系进行了大规模分析。结果表明,所提出的模型达到了较高的准确率(DSN: 85.2%, MNet: 86.3%)和Cohen's Kappa (DSN: 0.77, MNet: 0.79),表明与人类专家睡眠评分基本一致。此外,深度神经网络学习到的特征显示出超出传统使用的睡眠阶段的连续睡眠。催眠图分析显示,随着年龄的增长,NREM 3和REM睡眠的比例减少,NREM 2睡眠的比例增加。结果表明,四通道可穿戴式脑电头带为大规模的睡眠自动评分和分析提供了低成本和强大的手段。
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