Deep-Learning-Based Automated REM Sleep Detection in Patients With REM Sleep Behavior Disorder: Is It Reliable?
Year: 2025
Type: article
Abstract: BACKGROUND AND PURPOSE: Rapid eye movement (REM) sleep without atonia makes it difficult to detect REM sleep stages using electromyography in patients with REM sleep behavior disorder (RBD). The objectives of this study were to propose an automated REM sleep detector that requires only electroencephalography (EEG) and electrooculography (EOG) data, and to evaluate its performance using real-world polysomnography (PSG) data in RBD patients. METHODS: =64). An automated computerized REM detection algorithm was implemented using U-Sleep's publicly available pretrained network. RESULTS: =0.007). The REM sleep detector accurately classified REM sleep in the order of healthy controls, PD without RBD, iRBD, and PD with RBD, with AUC values of 0.94±0.02, 0.92±0.03, 0.90±0.02, and 0.86±0.02, respectively. CONCLUSIONS: Our U-Sleep-based REM sleep detector based on only EEG and EOG data showed good performance in detecting REM sleep. However, it performed considerably worse in RBD, especially in PD with RBD. Using transfer learning with fine-tuning by expert review, a high-performance REM sleep-detecting system will be realized.
Source: Journal of Clinical Neurology
Institutions: Kyung Hee University, Kyung Hee University Hospital at Gangdong, Korea Advanced Institute of Science and Technology, The Catholic University of Korea Uijeongbu St. Mary's Hospital, Catholic University of Korea, The Catholic University of Korea Incheon St. Mary's Hospital, The Catholic University of Korea Yeouido St. Mary's Hospital, The Catholic University of Korea Seoul St. Mary's Hospital
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
Award: G2485280701
OpenAlex ID: W4412766390