# Generalizable Sleep Staging via Multi-Level Domain Alignment

arXiv: https://arxiv.org/abs/2401.05363 · HTML: https://mlsanity.com/paper/2401.05363/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Domain Generalization Using a Mixture of Multiple Latent Domains](https://mlsanity.com/paper/1911.07661/) (2019)
- [Domain Generalization via Conditional Invariant Representation](https://mlsanity.com/paper/1807.08479/) (2018)
- [Model-Based Domain Generalization](https://mlsanity.com/paper/2102.11436/) (2021)
- [Visual Domain Adaptation with Manifold Embedded Distribution Alignment](https://mlsanity.com/paper/1807.07258/) (2018)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [Transfer Learning with Dynamic Distribution Adaptation](https://mlsanity.com/paper/1909.08531/) (2019)
- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)

## Built on by (most cited first, 17 of 17)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [Brant-X: A Unified Physiological Signal Alignment Framework](https://mlsanity.com/paper/2409.00122/) (2024)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2412.12159/) (2024)
- [sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals](https://mlsanity.com/paper/2602.13857/) (2026)
- [EEGAgent: A Unified Framework for Automated EEG Analysis Using Large Language Models](https://mlsanity.com/paper/2511.09947/) (2025)
- [MEASURE: Multi-scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Staging](https://mlsanity.com/paper/2510.12070/) (2025)
- [STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification](https://mlsanity.com/paper/2605.06736/) (2026)
- [Multi-Channel Differential Transformer for Cross-Domain Sleep Stage Classification with Heterogeneous EEG and EOG](https://mlsanity.com/paper/2508.15215/) (2025)
- [Bridging Accuracy and Explainability in EEG-based Graph Attention Network for Depression Detection](https://mlsanity.com/paper/2511.05537/) (2025)
- [Rethinking Time Series Domain Generalization via Structure-Stratified Calibration](https://mlsanity.com/paper/2603.02756/) (2026)
- [SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging](https://mlsanity.com/paper/2603.26738/) (2026)
- [Towards Multi-Source Domain Generalization for Sleep Staging with Noisy Labels](https://mlsanity.com/paper/2604.10009/) (2026)
- [Atoms of Thought: Universal EEG Representation Learning with Microstates](https://mlsanity.com/paper/2605.20182/) (2026)
- [SleepBand: Single-Source Domain Generalization for Sleep Staging via Physiologically Structured Spectral Modeling](https://mlsanity.com/paper/2607.04851/) (2026)
- [SemPSG: A Semantic Channel-Aware Foundation Model for Polysomnography Analysis](https://mlsanity.com/paper/2609.36619/) (2026)

## Abstract (arXiv)

Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets. Inspired by existing domain generalization methods, we adopt the feature alignment idea and propose a framework called SleepDG to solve it. Considering both of local salient features and sequential features are important for sleep staging, we propose a Multi-level Feature Alignment combining epoch-level and sequence-level feature alignment to learn domain-invariant feature representations. Specifically, we design an Epoch-level Feature Alignment to align the feature distribution of each single sleep epoch among different domains, and a Sequence-level Feature Alignment to minimize the discrepancy of sequential features among different domains. SleepDG is validated on five public datasets, achieving the state-of-the-art performance.