Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

OBJECTIVE: Evidence 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. METHODS: Analysis 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. RESULTS: Automated 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. CONCLUSIONS: This 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. SIGNIFICANCE: This 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

Clin Neurophysiol

Publication Date

04/2019

Volume

130

Pages

505 - 514

Keywords

Automated sleep staging, Electromyography, Parkinson’s disease, Polysomnography, RBD, REM sleep behaviour disorder, Sleep diagnostic tool, Aged, Algorithms, Automation, Electroencephalography, Electromyography, Electrooculography, Female, Humans, Male, Middle Aged, Polysomnography, REM Sleep Behavior Disorder, Reproducibility of Results, Sensitivity and Specificity, Sleep, REM