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

Mikkelsen, Kaare, Ebajemito, James K., Bonmati‐Carrion, Mari, Santhi, Nayantara, Revell, Victoria, Atzori, Giuseppe, della Monica, Ciro, Debener, Stefan, Dijk, Derk-Jan, Sterr, Annette and Vos, Maarte (2019) Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy Journal of Sleep Research, 28 (2), e12786.

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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.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Psychology
Authors :
NameEmailORCID
Mikkelsen, Kaare
Ebajemito, James K.j.ebajemito@surrey.ac.uk
Bonmati‐Carrion, Mari
Santhi, NayantaraN.Santhi@surrey.ac.uk
Revell, VictoriaV.Revell@surrey.ac.uk
Atzori, GiuseppeG.Atzori@surrey.ac.uk
della Monica, Ciroc.dellamonica@surrey.ac.uk
Debener, Stefan
Dijk, Derk-JanD.J.Dijk@surrey.ac.uk
Sterr, AnnetteA.Sterr@surrey.ac.uk
Vos, Maarte
Date : April 2019
Funders : EPSRC - Engineering and Physical Sciences Research Council, SCNi, Wellcome Trust, Circadian Therapeutics, NIHR Oxford Biomedical Research Centre
DOI : 10.1111/jsr.12786
Copyright Disclaimer : © 2018 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords : Automated sleep scoring; Ear EEG; EEG; Home EEG; Mobile EEG
Depositing User : Diane Maxfield
Date Deposited : 01 Nov 2019 11:17
Last Modified : 01 Nov 2019 11:17
URI: http://epubs.surrey.ac.uk/id/eprint/853017

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