EEG recordings as a source for the detection of IRBD

Sissel Bisgaard, Bolette Duun-Christensen, Lykke Kempfner, Helge Bjarup Dissing Sørensen, Poul Jennum

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The purpose of this pilot study was to develop a supportive algorithm for the detection of idiopathic Rapid Eye-Movement (REM) sleep Behaviour Disorder (iRBD) from EEG recordings. iRBD is defined as REM sleep without atonia with no current sign of neurodegenerative disease, and is one of the earliest known biomarkers of Parkinson's Disease (PD). It is currently diagnosed by polysomnography (PSG), primarily based on EMG recordings during REM sleep. The algorithm was developed using data collected from 42 control subjects and 34 iRBD subjects. A feature was developed to represent high amplitude contents of the EEG and a semi-automatic signal reduction method was introduced. The reduced feature set was used for a subject-based classification. With a subject specific re-scaling of the feature set and the use of an outlier detection classifier the algorithm reached an accuracy of 0.78. The result shows that EEG recordings contain valid information for a supportive algorithm for the detection of iRBD. Further investigation could lead to promising application of EEG recordings as a supportive source for the detection of iRBD.
Original languageEnglish
Title of host publicationProceedings of 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Publication date2015
Pages606-609
ISBN (Print)9781424492718
DOIs
Publication statusPublished - 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Milano, Italy
Duration: 25 Aug 201529 Aug 2015
Conference number: 37

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Number37
Country/TerritoryItaly
CityMilano
Period25/08/201529/08/2015

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