# DualStabSleepNet: A Dual-Domain Diffusion Stabilization Network for Robust Sleep Staging

arXiv: https://arxiv.org/abs/2609.27793 · HTML: https://mlsanity.com/paper/2609.27793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 2 of 2)

- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models](https://mlsanity.com/paper/2407.20253/) (2024)

## Abstract (arXiv)

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.