Deep-Learning-Based Automated REM Sleep Detection in Patients With REM Sleep Behavior Disorder: Is It Reliable?
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.
Authors
- Yun Ho Choi (ORCID: https://orcid.org/0000-0001-6463-2857)
- Woojun Kim (ORCID: https://orcid.org/0000-0001-8204-8881)
- Jaeseung Jeong (ORCID: https://orcid.org/0000-0002-0827-2735)
- Dong-Woo Ryu (ORCID: https://orcid.org/0000-0003-3390-4607)
- Sunil L. Kim (ORCID: https://orcid.org/0000-0003-2684-4335)
- Seonghoon Kim (ORCID: https://orcid.org/0000-0002-2077-309X)
- Yu Jin Jung (ORCID: https://orcid.org/0000-0003-4643-1968)
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- The Catholic University of Korea Uijeongbu St. Mary's Hospital (KR)
- The Catholic University of Korea Yeouido St. Mary's Hospital (KR)
- Kyung Hee University Hospital at Gangdong (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Journal of Clinical Neurology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3988/jcn.2025.0053
- Citations
- 1
- Primary Topic
- Sleep and Wakefulness Research
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- National Research Foundation of Korea