Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy
Kaare B Mikkelsen, James K Ebajemito, Maria A Bonmati-Carrion, Nayantara Santhi, Victoria L Revell, Giuseppe Atzori, Ciro Della Monica, Stefan Debener, Derk-Jan Dijk, Annette Sterr, Maarten de Vos, Kaare B Mikkelsen, James K Ebajemito, Maria A Bonmati-Carrion, Nayantara Santhi, Victoria L Revell, Giuseppe Atzori, Ciro Della Monica, Stefan Debener, Derk-Jan Dijk, Annette Sterr, Maarten de Vos
Abstract
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low-cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex-printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self-applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier ("random forests") and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter-individual variation in sleep parameters. The results demonstrate that machine-learning-based scoring of around-the-ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine-learning-based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine-learning-based scoring holds promise for large-scale sleep studies.
Keywords: EEG; automated sleep scoring; ear EEG; home EEG; mobile EEG.
Conflict of interest statement
JKE, MABC, NS, VLR, SD, DJD and AS declare no conflicts of interest, including any involvement in organizations with a financial interest in the subject matter of the paper. KBM and MDV received a grant from Circadian Therapeutics to perform this study. MDV is a founding member of Circadian Therapeutics.
© 2018 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.
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