通过选择性对齐域数据，提升睡眠分期模型在跨人群中的泛化能力。
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment
- 用MRCNN提取脑电特征，结合EMD筛选与目标域相似的源域数据
- 在域偏移场景下准确率显著优于基线方法，提升模型鲁棒性
- 适合临床部署中面对不同人群、设备环境的睡眠分析任务
实际睡眠阶段分类中，受试者间生理差异、年龄、健康状况及记录条件不同，导致脑电信号存在显著域偏移，影响模型准确性与可靠性，尤其在新数据分布下易出现负迁移。为此，本文提出SelectiveFinetuning方法：利用预训练的多分辨率卷积神经网络（MRCNN）提取睡眠阶段特征；设计基于地球移动距离（EMD）的域对齐机制，评估并筛选与目标域相近的源域数据；仅使用匹配数据微调模型，有效缓解域偏移问题。实验表明，该方法在域偏移场景下性能超越现有基线，具备更强的适应性与鲁棒性，适用于数据分布不确定的实际应用场景。
原文摘要 · Abstract (English)
In practical sleep stage classification, a key challenge is the variability of EEG data across different subjects and environments. Differences in physiology, age, health status, and recording conditions can lead to domain shifts between data. These domain shifts often result in decreased model accuracy and reliability, particularly when the model is applied to new data with characteristics different from those it was originally trained on, which is a typical manifestation of negative transfer. To address this, we propose SelectiveFinetuning in this paper. Our method utilizes a pretrained Multi Resolution Convolutional Neural Network (MRCNN) to extract EEG features, capturing the distinctive characteristics of different sleep stages. To mitigate the effect of domain shifts, we introduce a domain aligning mechanism that employs Earth Mover Distance (EMD) to evaluate and select source domain data closely matching the target domain. By finetuning the model with selective source data, our SelectiveFinetuning enhances the model's performance on target domain that exhibits domain shifts compared to the data used for training. Experimental results show that our method outperforms existing baselines, offering greater robustness and adaptability in practical scenarios where data distributions are often unpredictable.
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