Generalizable Sleep Staging via Multi-Level Domain Alignment
Jiquan Wang, Sha Zhao, Haiteng Jiang, Shijian Li, Tao Li, Gang Pan

TL;DR
This paper introduces a domain generalization framework called SleepDG for automatic sleep staging, which aligns features at multiple levels to improve model generalization across unseen datasets, achieving state-of-the-art results.
Contribution
The paper proposes a novel multi-level feature alignment framework for domain generalization in sleep staging, addressing the challenge of unseen dataset variability.
Findings
SleepDG outperforms existing methods on five public datasets.
Multi-level feature alignment improves domain invariance.
State-of-the-art performance achieved across diverse datasets.
Abstract
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…
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Code & Models
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Taxonomy
TopicsObstructive Sleep Apnea Research
MethodsALIGN
