Automated Sleep Staging on Wearable EEG Enables Sleep Analysis at Scale
Maurice Abou Jaoude, Aravind Ravi, Jiansheng Niu, Hubert Banville, Nicolas Florez Torres, Christopher Aimone
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.