DualStabSleepNet：用于稳健睡眠分期的双域扩散稳定网络
DualStabSleepNet: A Dual-Domain Diffusion Stabilization Network for Robust Sleep Staging
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- Shandong University of Science and Technology（山东科技大学）
- South China University of Technology（华南理工大学）
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中文总结 AI 辅助
提出DSSNet双域扩散稳定网络，通过数据域去噪和特征域稳定提升睡眠分期鲁棒性，在四个公开数据集上取得最先进准确率并显著改善过渡阶段识别。
中文摘要 AI 辅助
现有的自动睡眠分期深度学习方法在异构记录条件下鲁棒性有限，其中非平稳噪声、受试者间差异和跨数据集分布偏移导致特征不稳定和泛化能力差。本工作提出DualStabSleepNet（DSSNet），一种用于稳健睡眠分期的双域扩散稳定网络，在数据和特征两个域中均提高鲁棒性。在对多导睡眠图（PSG）进行预处理后，一个基于连续尺度扩散的稳定模块在抑制噪声的同时保留生理信号结构。稳定的信号被转换为时频表示，并输入到Vision Transformer骨干网络中。一个教师-学生引导的扩散特征稳定模块进一步减轻特征漂移并强制多级特征一致性。在四个公开PSG数据集SleepEDF-20、SleepEDF-78、SHHS和ISRUC-S3上评估，DSSNet达到了最先进的准确率89.2%、88.0%、89.7%、86.7%，并提高了宏F1和Cohen's kappa。它在困难的过渡阶段取得了显著改进（例如，SHHS上N1的12.5%增益），并提升了N2/REM识别。在跨数据集设置下，DSSNet对分布偏移具有鲁棒性，其性能与目标数据集训练的基线相当或更优，展示了其在异构队列中实际睡眠分期的应用潜力。
英文摘要
Existing deep learning approaches for automatic sleep staging suffer from limited robustness under heterogeneous recording conditions, where non-stationary noise, inter-subject differences and cross-dataset distribution shifts cause unstable features and poor generalization. This work proposes DualStabSleepNet (DSSNet), a dual-domain diffusion stabilization network for robust sleep staging, which improves robustness in both data and feature domains. After preprocessing multi-channel polysomnography (PSG), a continuous-scale diffusion-based stabilization module suppresses noise while preserving physiological signal structures. Stabilized signals are converted to time-frequency representations and fed into a Vision Transformer backbone. A teacher-student guided diffusion feature stabilization module further mitigates feature drift and enforces multi-level feature consistency. Evaluated on four public PSG datasets SleepEDF-20, SleepEDF-78, SHHS and ISRUC-S3, DSSNet achieves state-of-the-art accuracy of 89.2%, 88.0%, 89.7%, 86.7% with improved macro-F1 and Cohen's kappa. It obtains notable improvements on hard transitional stages (e.g., 12.5% gain for N1 on SHHS) and boosts N2/REM recognition. Under cross-dataset settings, DSSNet is robust to distribution shift and performs on par with or superior to target-dataset trained baselines, demonstrating its practical potential for real-world sleep staging across heterogeneous cohorts.
