Minimal-Input Deep Learning for Remote Screening of REM Sleep Behavior Disorder

Abstract This work investigates whether a deep learning model with minimal inputs can accurately identify Rapid Eye Movement Sleep Behavioral Disorder (RBD). We propose an interpretable two-step approach using two convolutional neural networks for sleep staging and RBD classification. Experiments on data from 18 RBD participants and 178 healthy controls demonstrate that reliable classification can be achieved using frontal electroencephalogram (EEG) and electrooculogram (EOG) input signals. GradCAM attention reveals a 22% increase in importance in the 9-22 Hz band of EOG for RBD cases. Our findings highlight the potential for remote, wearable-based RBD screening at home.