Muscle Activity Detection during Sleep by Ear-EEG

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Muscle activation during sleep is an important biomarker in the diagnosis of several sleep disorders and neurodegenerative diseases. Muscle activity is typically assessed manually based on the EMG channels from polysomnography recordings. Ear-EEG provides a mobile and comfortable alternative for sleep assessment. In this study, ear-EEG was used to automatically detect muscle activities during sleep. The study was based on a dataset comprising four full night recordings from 20 healthy subjects with concurrent polysomnography and ear-EEG. A binary label, active or relax, extracted from the chin EMG was assigned to selected 30 s epoch of the sleep recordings in order to train a classifier to predict muscle activation. We found that the ear-EEG based classifier detected muscle activity with an accuracy of 88% and a Cohen's kappa value of 0.71 relative to the labels derived from the chin EMG channels. The analysis also showed a significant difference in the distribution of muscle activity between REM and non-REM sleep.

OriginalsprogEngelsk
Titel42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC)
Antal sider4
UdgivelsesstedMontreal, QC
ForlagIEEE
Udgivelsesårjul. 2020
Sider1007-1010
ISBN (Elektronisk)9781728119908
DOI
StatusUdgivet - jul. 2020
Begivenhed42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Varighed: 20 jul. 202024 jul. 2020

Konference

Konference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
LandCanada
ByMontreal
Periode20/07/202024/07/2020
SerietitelProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Vol/bind2020-July
ISSN1557-170X

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