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ObjectiveEvidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification.MethodsAnalysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent.ResultsAutomated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved from 86% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging.ConclusionsThis study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation.SignificanceThis study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.

Original publication

DOI

10.1016/j.clinph.2019.01.011

Type

Journal article

Journal

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

Publication Date

04/2019

Volume

130

Pages

505 - 514

Addresses

University of Oxford, Institute of Biomedical Engineering, Dept. Engineering Sciences, Oxford, UK. Electronic address: navin.cooray@eng.ox.ac.uk.

Keywords

Humans, REM Sleep Behavior Disorder, Electroencephalography, Electrooculography, Electromyography, Polysomnography, Sensitivity and Specificity, Reproducibility of Results, Sleep, REM, Algorithms, Automation, Aged, Middle Aged, Female, Male