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.
@article{arxiv.2412.12159,
title = {Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation},
author = {Yangxuan Zhou and Sha Zhao and Jiquan Wang and Haiteng Jiang and hijian Li and Benyan Luo and Tao Li and Gang Pan},
journal= {arXiv preprint arXiv:2412.12159},
year = {2024}
}