  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 investigates 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. 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 (RF) 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 metrics. The EOG and EMG combination
provided the best minimalist fully automated performance, achieving
$0.57\pm0.19$ kappa (3 stage) for sleep staging and an RBD detection accuracy
of $0.90\pm0.11$, (sensitivity, and specificity $0.88\pm0.13$, and
$0.92\pm0.098$). A single ECG sensor allowed three state sleep staging with
$0.28\pm0.06$ kappa and RBD detection accuracy of $0.62\pm0.10$. This study
demonstrated the feasibility of using signals from a single EOG and EMG sensor
to detect RBD using fully-automated techniques. This study proposes a
cost-effective, practical, and simple RBD identification support tool using
only two sensors (EMG and EOG), ideal for screening purposes.
