在REM睡眠行为障碍患者中基于深度学习的自动REM睡眠检测:可靠吗?
Yu Jin Jung1, Sunil Kim2, Yun Ho Choi3
1Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Korea.
Journal of clinical neurology (Seoul, Korea)
|August 29, 2025
概括
使用脑电图 (EEG) 和眼电图 (EOG) 的自动化系统有效地检测快速眼动 (REM) 睡眠. 在REM睡眠行为障碍 (RBD) 患者,特别是患有帕金森病 (PD) 患者的表现较低.
科学领域:
- 神经科学
- 睡眠医学
- 医疗保健中的人工智能
背景情况:
- 在REM睡眠行为障碍 (RBD) 中检测REM睡眠是具有挑战性的,因为没有肌肉衰竭.
- 目前的方法通常依赖于电肌图 (EMG),在RBD中可能不可靠.
研究的目的:
- 开发一个只使用EEG和EOG数据的自动REM睡眠探测器.
- 使用多睡眠图 (PSG) 数据评估探测器在RBD患者的性能.
主要方法:
- 使用了5家医院的310个PSG数据集,包括RBD (n=200) 和非RBD (n=110) 组.
- 使用基于U-Sleep预训练网络的自动REM检测算法.
- 数据被分为具有RBD的帕金森病 (PD),没有RBD的PD,特异性RBD (iRBD) 和健康对照.
主要成果:
- 在REM睡眠检测中,U-Sleep算法获得了0. 90±0. 14的接收器运行特征曲线 (AUC) 下的总面积.
- 在RBD (AUC=0. 88±0. 13) 和非RBD (AUC=0. 93±0. 14) 组之间,表现有显著差异 (p=0. 007).
- 检测准确度按照健康对照 (0. 94±0. 02),没有RBD的PD (0. 92±0. 03),iRBD (0. 90±0. 02) 和具有RBD的PD (0. 86±0. 02) 的顺序进行.
结论:
- 基于EEG/EOG的自动REM睡眠探测器表现良好.
- 在RBD患者,尤其是PD患者中,该系统的准确性降低.
- 建议使用转移学习和专家微调来提高系统性能.
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