In this article, the authors introduce the concept of domain generalization into automatic sleep staging, which is important for sleep assessment and disorder diagnosis. They highlight that most existing methods for sleep staging rely on specific datasets and cannot be easily generalized to unseen datasets. To address this issue, they propose the task of generalizable sleep staging and present a framework called SleepDG to achieve this.
The authors draw inspiration from existing domain generalization methods and adopt the idea of feature alignment. They argue that considering both local salient features and sequential features is crucial for accurate sleep staging. To tackle this, they propose a Multi-level Feature Alignment approach that combines epoch-level and sequence-level feature alignment to learn domain-invariant feature representations.
To align the feature distribution of each sleep epoch among different domains, the authors design an Epoch-level Feature Alignment method. This helps to ensure that the features extracted from individual sleep epochs are consistent across datasets. Additionally, they introduce a Sequence-level Feature Alignment technique that minimizes the discrepancy of sequential features between different domains.
The proposed SleepDG framework is evaluated on five public datasets and achieves state-of-the-art performance in sleep staging. This demonstrates its effectiveness in improving the generalization ability of sleep staging models to unseen datasets.
Overall, the authors’ work on introducing domain generalization into automatic sleep staging is significant as it addresses an important limitation of existing methods. By leveraging feature alignment techniques, SleepDG provides a promising solution for improving the generalization ability of sleep staging models. Future research in this area could explore additional techniques for domain generalization and investigate the application of SleepDG in real-world clinical settings.