A Deep Learning Approach for Classifying Rapid Eye Movement Sleep Behavior Disorder Using EEGNet
Yun Ho Choi1, Sunil Kim2, Jaeseung Jeong3
1Department of Neurology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Background And Purpose:
Rapid eye movement (REM) sleep behavior disorder (RBD) is a prodromal marker for α-synucleinopathies, and its early and accurate diagnosis provides invaluable insights into the development of neurodegenerative diseases. This study aimed to propose a deep-learning-based automated framework for RBD classification and to evaluate its performance using real-world, multi-cohort polysomnography (PSG) data.
Methods:
REM electromyogram (EMG) data of 227 PSG recordings collected across five tertiary hospitals were analyzed. The dataset was divided into RBD (n=156) and non-RBD (n=71) and further stratified into Parkinson's disease (PD) with RBD (n=60), PD without RBD (n=24), isolated RBD (n=96), and healthy controls (n=47). An automated framework for RBD classification, based on manual and automated sleep staging, was implemented using EEGNet.
Results:
The EEGNet-based classifier achieved a receiver operating characteristics-area under the curve of 0.812 (95% confidence interval [CI], 0.744-0.871) using manual staging, which was significantly higher than the 0.777 (95% CI, 0.704-0.843) achieved with the automated U-Sleep approach (p=0.039). However, relying solely on REM EMG data proved insufficient to accurately distinguish among the four clinical subgroups.
Conclusions:
Our EEGNet-based RBD classifier, focused solely on EMG data, demonstrated reasonable performance in detecting RBD. This proposed model has the potential to reduce clinicians' workload and support diagnostic decision-making and enabling more accurate RBD classification. This multi-center study highlights the feasibility of an automated and generalizable RBD detection framework, paving the way for broader clinical implementation.
