Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers

Lees, T; Chalmers, T; Burton, D; Zilberg, E; Penzel, T; Lal, S

Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers

Lees, T

Chalmers, T Burton, D Zilberg, E Penzel, T Lal, S

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Publication Type:: Journal Article
Citation:: Physiological Measurement, 2018, 39 (10)
Issue Date:: 2018-10-30

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Field	Value	Language
dc.contributor.author	Lees, T https://orcid.org/0000-0002-7321-2704	en_US
dc.contributor.author	Chalmers, T	en_US
dc.contributor.author	Burton, D	en_US
dc.contributor.author	Zilberg, E	en_US
dc.contributor.author	Penzel, T	en_US
dc.contributor.author	Lal, S https://orcid.org/0000-0002-0911-0850	en_US
dc.date.issued	2018-10-30	en_US
dc.identifier.citation	Physiological Measurement, 2018, 39 (10)	en_US
dc.identifier.issn	0967-3334	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130285
dc.description.abstract	© 2018 Institute of Physics and Engineering in Medicine. Objective: In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers. Approach: Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20). Main results: Self-reported fatigue/sleepiness scores of the train driver cohort were primarily associated with EEG delta, theta, and alpha variables; however, some beta and gamma associations were also implicated. Furthermore, general linear models informed by these EEG variables were able to predict self-reported scores with varying degrees of success, representing between 48% and 54% of variance in fatigue scores. Significance: Self-reported fatigue/sleepiness scores of train drivers were predicted with varying degrees of success (dependent upon the self-reported fatigue/sleepiness measure) by alterations to monopolar delta, theta, and alpha band activity variables, indicating EEG as a potential indicator for fatigue/sleepiness in train drivers.	en_US
dc.relation.ispartof	Physiological Measurement	en_US
dc.relation.isbasedon	10.1088/1361-6579/aae42e	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Brain	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Fatigue	en_US
dc.subject.mesh	Diagnosis, Computer-Assisted	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Boredom	en_US
dc.subject.mesh	Transportation	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Young Adult	en_US
dc.subject.mesh	Self Report	en_US
dc.subject.mesh	Diagnostic Self Evaluation	en_US
dc.subject.mesh	Sleepiness	en_US
dc.title	Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	39	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	1116 Medical Physiology	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

© 2018 Institute of Physics and Engineering in Medicine. Objective: In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers. Approach: Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20). Main results: Self-reported fatigue/sleepiness scores of the train driver cohort were primarily associated with EEG delta, theta, and alpha variables; however, some beta and gamma associations were also implicated. Furthermore, general linear models informed by these EEG variables were able to predict self-reported scores with varying degrees of success, representing between 48% and 54% of variance in fatigue scores. Significance: Self-reported fatigue/sleepiness scores of train drivers were predicted with varying degrees of success (dependent upon the self-reported fatigue/sleepiness measure) by alterations to monopolar delta, theta, and alpha band activity variables, indicating EEG as a potential indicator for fatigue/sleepiness in train drivers.

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http://hdl.handle.net/10453/130285