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Proof of concept: Screening for REM sleep behaviour disorder with a minimal set of sensors

Authors :
Mkael Symmonds
Navin Cooray
Christine Lo
Fernando Andreotti
Maarten De Vos
Michele T.M. Hu
Source :
Clinical Neurophysiology
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

Highlights • We demonstrate the feasibility of a fully automated REM sleep behaviour (RBD) screening tool using minimal sensors. • REM sleep is detected accurately and reliably in individuals with RBD, without EEG sensors. • Automated sleep staging was able to classify REM sleep with sufficient accuracy to allow for the accurate detection of RBD.<br />Objective Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. This study investigated the use of a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors. Methods Polysomnography signals from 50 participants with RBD and 50 age-matched healthy controls were used to evaluate this study. Three stage sleep classification was achieved using a random forest classifier and features derived from a combination of cost-effective and easy to use sensors, namely electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG) channels. Subsequently, RBD detection was achieved using established and new metrics derived from ECG and EMG channels. Results The EOG and EMG combination provided the optimal minimalist fully-automated performance, achieving 0.57 ± 0.19 kappa (3 stage) for sleep staging and an RBD detection accuracy of 0.90 ± 0.11, (sensitivity and specificity of 0.88 ± 0.13 and 0.92 ± 0.098, respectively). A single ECG sensor achieved three state sleep staging with 0.28 ± 0.06 kappa and RBD detection accuracy of 0.62 ± 0.10. Conclusions This study demonstrates the feasibility of using signals from a single EOG and EMG sensor to detect RBD using fully-automated techniques. Significance This study proposes a cost-effective, practical, and simple RBD identification support tool using only two sensors (EMG and EOG); ideal for screening purposes.

Details

ISSN :
13882457
Volume :
132
Database :
OpenAIRE
Journal :
Clinical Neurophysiology
Accession number :
edsair.doi.dedup.....b6ee9ca8f1b4397ed4ba010619129d58
Full Text :
https://doi.org/10.1016/j.clinph.2021.01.009