Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation
This work addresses the issue of individual differences in sleep staging for clinical applications, representing an incremental improvement through domain adaptation.
The paper tackled the problem of poor generalization in deep learning models for automatic sleep staging by proposing a Source-Free Unsupervised Individual Domain Adaptation framework, which achieved state-of-the-art performance on three public datasets.
Sleep staging is crucial for assessing sleep quality and diagnosing related disorders. Recent deep learning models for automatic sleep staging using polysomnography often suffer from poor generalization to new subjects because they are trained and tested on the same labeled datasets, overlooking individual differences. To tackle this issue, we propose a novel Source-Free Unsupervised Individual Domain Adaptation (SF-UIDA) framework. This two-step adaptation scheme allows the model to effectively adjust to new unlabeled individuals without needing source data, facilitating personalized customization in clinical settings. Our framework has been applied to three established sleep staging models and tested on three public datasets, achieving state-of-the-art performance.