无需源数据即可个性化适配新患者，提升睡眠分期模型泛化能力。
Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation
- 提出无源无监督个体域适应框架，仅用新患者未标注数据调优。
- 在三个公开数据集上均达当前最佳性能，验证跨个体迁移有效性。
- 适合临床个性化睡眠分析，尤其适用于缺乏标签数据的场景。
睡眠分期对评估睡眠质量及诊断相关疾病至关重要。近期基于多导睡眠图的深度学习自动睡眠分期模型常因训练与测试使用同一标注数据集，导致对新受试者泛化能力差，忽略个体差异。为解决此问题，我们提出一种新型无源无监督个体域适应（SF-UIDA）框架。该两步适配方案使模型能在不依赖源数据的情况下，仅利用新个体的未标注数据有效调整自身，实现临床环境中的个性化定制。本框架已应用于三个主流睡眠分期模型，并在三个公共数据集上进行测试，均取得当前最优性能。
原文摘要 · Abstract (English)
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.
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