Summary of Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation, by Yangxuan Zhou et al.
Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation
by Yangxuan Zhou, Sha Zhao, Jiquan Wang, Haiteng Jiang, hijian Li, Benyan Luo, Tao Li, Gang Pan
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Source-Free Unsupervised Individual Domain Adaptation (SF-UIDA) framework addresses the limitation of existing deep learning models for automatic sleep staging by allowing personalized customization without requiring source data. By adapting to new unlabeled individuals, this two-step adaptation scheme enhances generalization and improves performance in clinical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SF-UIDA framework helps improve sleep staging accuracy by allowing the model to adapt to new subjects without needing labeled data from previous ones. This can be especially useful for diagnosing sleep disorders and assessing sleep quality. The framework was tested on three public datasets, achieving state-of-the-art performance with established sleep staging models. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation » Generalization » Unsupervised
