Publication:
Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment

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cris.virtual.author-orcid0000-0001-6088-2015
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cris.virtualsource.author-orcide7fb926b-5175-4bde-bb78-d2912cfc4f0b
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cris.virtualsource.author-orcid32a15133-960d-49a1-a9c7-6606fad47c57
cris.virtualsource.author-orcid83d50954-f036-45d9-ad7f-e371f8a5098b
dc.contributor.authorSkorucak, Jelena
dc.contributor.authorGodeschalk, Anneke Grietje Elizabeth
dc.contributor.authorAchermann, Peter
dc.contributor.authorMathis, Johannes
dc.contributor.authorSchreier, David Raphael
dc.date.accessioned2024-10-05T09:51:54Z
dc.date.available2024-10-05T09:51:54Z
dc.date.issued2020-01-23
dc.description.abstractStudy Objectives: Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes. In the diagnostic sleep-wake and fitness-to-drive assessment, accurate and early identification of sleepiness is essential. However, in the absence of a standardised definition and a time-efficient scoring method of MSEs, these short fragments are not assessed in clinical routine. Based on data of moderately sleepy patients, we recently developed the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring of MSEs and corresponding machine learning algorithms for automatic MSE detection, both mainly based on the electroencephalogram (EEG). The present study aimed to investigate the relationship between automatically detected MSEs and driving performance in a driving simulator, recorded in parallel with EEG, and to assess algorithm performance for MSE detection in severely sleepy participants. Methods: Maintenance of wakefulness test (MWT) and driving simulator recordings of 18 healthy participants, before and after a full night of sleep deprivation, were retrospectively analysed. Performance of automatic detection was compared with visual MSE scoring, following the BERN criteria, in MWT recordings of 10 participants. Driving performance was measured by the standard deviation of lateral position and the occurrence of off-road events. Results: In comparison to visual scoring, automatic detection of MSEs in participants with severe sleepiness showed good performance (Cohen's kappa = 0.66). The MSE rate in the MWT correlated with the latency to the first MSE in the driving simulator (r s = -0.54, p < 0.05) and with the cumulative MSE duration in the driving simulator (r s = 0.62, p < 0.01). No correlations between MSE measures in the MWT and driving performance measures were found. In the driving simulator, multiple correlations between MSEs and driving performance variables were observed. Conclusion: Automatic MSE detection worked well, independent of the degree of sleepiness. The rate and the cumulative duration of MSEs could be promising sleepiness measures in both the MWT and the driving simulator. The correlations between MSEs in the driving simulator and driving performance might reflect a close and time-critical relationship between sleepiness and performance, potentially valuable for the fitness-to-drive assessment.
dc.description.sponsorshipUniversitätsklinik für Neurologie
dc.identifier.doi10.7892/boris.145008
dc.identifier.pmid32038155
dc.identifier.publisherDOI10.3389/fnins.2020.00008
dc.identifier.urihttps://boris-portal.unibe.ch/handle/20.500.12422/55069
dc.language.isoen
dc.publisherFrontiers Research Foundation
dc.relation.ispartofFrontiers in neuroscience
dc.relation.issn1662-4548
dc.relation.organizationDCD5A442BAE0E17DE0405C82790C4DE2
dc.subject.ddc600 - Technology::610 - Medicine & health
dc.titleAutomatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment
dc.typearticle
dspace.entity.typePublication
dspace.file.typetext
oaire.citation.startPage8
oaire.citation.volume14
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oairecerif.author.affiliationUniversitätsklinik für Neurologie
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oairecerif.author.affiliationUniversitätsklinik für Neurologie
oairecerif.author.affiliationUniversitätsklinik für Neurologie
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unibe.date.licenseChanged2020-08-11 01:18:00
unibe.description.ispublishedpub
unibe.eprints.legacyId145008
unibe.journal.abbrevTitleFront Neurosci
unibe.refereedTRUE
unibe.subtype.articlejournal

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