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.
Journal of Clinical Neurology (Seoul, Korea)
|May 11, 2026
Summary
A deep learning model using EEGNet effectively detects REM sleep behavior disorder (RBD) from EMG data. This automated framework shows potential for aiding clinical diagnosis of RBD and related neurodegenerative diseases.
Area of Science:
- Neurology
- Sleep Medicine
- Artificial Intelligence in Healthcare
Background:
- Rapid eye movement (REM) sleep behavior disorder (RBD) is a key early indicator of α-synucleinopathies.
- Early diagnosis of RBD is crucial for understanding neurodegenerative disease progression.
Purpose of the Study:
- To develop and evaluate a deep learning-based automated framework for RBD classification.
- To assess the framework's performance on multi-center polysomnography (PSG) data.
Main Methods:
- Analysis of REM electromyogram (EMG) data from 227 PSG recordings across five hospitals.
- Implementation of an automated classification framework using EEGNet, comparing manual and automated sleep staging.
- Stratification of data into RBD, non-RBD, Parkinson's disease with/without RBD, isolated RBD, and healthy control groups.
Main Results:
- The EEGNet classifier achieved an AUC of 0.812 with manual staging, outperforming the U-Sleep automated approach (AUC 0.777).
- Solely using REM EMG data was insufficient for differentiating the four clinical subgroups accurately.
- The model demonstrated reasonable performance in detecting RBD.
Conclusions:
- The EEGNet-based classifier shows promise for reducing clinician workload and supporting RBD diagnostic decisions.
- This multi-center study validates an automated and generalizable framework for RBD detection.
- The findings support broader clinical implementation of automated RBD classification.
