睡眠MFormer:一个高效的注意力框架与对比学习为单通道EEG睡眠分期
Mingjie Li1,2, Jie Xia1,2, Jiadong Pan1,2
1State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou 311121, China.
Brain sciences
|January 28, 2026
概括
SleepMFormer使用高效的变压器编码器和对比学习来增强自动睡眠阶段分类. 这种方法显著减少了用于改善睡眠质量评估和疾病诊断的计算开销.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 睡眠阶段的分类对于诊断睡眠障碍和评估睡眠质量至关重要.
- 脑电图 (EEG) 是确定睡眠阶段的主要方法.
- 变压器模型提供了高性能,但遭受计算效率低下.
研究的目的:
- 推出SleepMFormer,一个高效的端到端框架,用于使用单通道EEG的自动睡眠阶段分类.
- 为了提高基于变压器的睡眠分期算法的效率.
- 在资源有限的环境中实现实用的睡眠分期.
主要方法:
- SleepMFormer使用简化的变压器编码器来提高注意力效率.
- 监督的对比学习在培训期间增强了表示的稳定性.
- 该框架旨在提供高效的培训和推断.
主要成果:
- 在公共数据集 (Sleep-EDF,PhysioNet,SHHS) 上,SleepMFormer 实现了竞争性的性能.
- 与标准变压器模型相比,它可将训练和推断时间缩短高达33%.
- 在不同的功能提取器 (DeepSleepNet,TinySleepNet) 中保持性能.
结论:
- SleepMFormer为自动睡眠分阶段提供了一个高效和实用的解决方案.
- 该框架显示了睡眠医学中临床应用的巨大潜力.
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