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J Sleep Res. 2019 Apr;28(2):e12786. doi: 10.1111/jsr.12786. Epub 2018 Nov 13.

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Author information

1
Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
2
Department of Engineering, Aarhus University, Aarhus, Denmark.
3
School of Psychology, University of Surrey, Surrey, UK.
4
Surrey Sleep Research Centre, University of Surrey, Surrey, UK.
5
Surrey Clinical Research Centre, University of Surrey, Surrey, UK.
6
Cluster of Excellence Hearing4All, Oldenburg, Germany.
7
Department of Psychology, University of Oldenburg, Oldenburg, Germany.

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

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