RimeSleepNet:用于s-EEG睡眠阶段分类的混合深度学习网络
Xiaolin Wang1, Xiaowei Li1, Jing Li2
1School of Mechanical Engineering and Automation, Shanghai University, Shanghai, 201900, China; Institute of Artificial Intelligence, Shanghai University, Shanghai, 201900, China.
Sleep medicine
|October 2, 2025
概括
这项研究介绍了RimeSleepNet,这是一种混合深度学习模型,可以有效地减少睡眠脑电图 (s-EEG) 信号中的频率别名,以准确地分类睡眠阶段. 它显著改善了用于诊断睡眠障碍的自动睡眠分析.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 睡眠阶段的分类对于睡眠研究和临床诊断至关重要.
- 睡眠电脑电图 (s-EEG) 信号中的频率别名对现有方法构成了持续的挑战.
- 准确的睡眠分期有助于诊断和管理睡眠障碍.
研究的目的:
- 开发一种新的混合深度学习模型,RimeSleepNet,以解决s-EEG信号中的频率别名.
- 提高自动睡眠阶段分类 (NREM,REM,Wake) 的准确性和稳定性.
- 为睡眠障碍诊断和个性化监测提供先进的工具.
主要方法:
- 拟议的RimeSleepNet模型将 rime优化算法与变化模式分解 (VMD) 结合起来,以生成内在模式函数 (IMF).
- 利用卷积神经网络 (CNN) 来从IMF中提取特征,然后使用多头自我注意 (MHSA) 来进行特征加权.
- 采用长期短期记忆 (LSTM) 网络来建模时间动态,用于最终的睡眠阶段分类.
- 在成都人民医院和睡眠EDF数据集上评估模型性能.
主要成果:
- RimeSleepNet获得了高的F1分数:0.94 (NREM),0.89 (REM) 和0.92 (WAKE),其AUC为0.92.
- 在睡眠阶段分类准确度方面,超越了包括CNN和LSTM在内的基线模型.
- 在数据集中表现出强大的泛化,科恩的kappa为0.90.
- 与LSTM相比,验证损失减少了53%,这表明学习效率有所提高.
结论:
- RimeSleepNet有效地减轻了s-EEG信号中的频率别名,从而实现了优越的睡眠阶段分类.
- 混合深度学习方法为临床环境中自动化睡眠分析提供了一个有希望的解决方案.
- 这种模型代表了诊断睡眠障碍的进步,并使个性化睡眠监测成为可能.
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