提出多尺度最小充分表示学习框架，提升睡眠分期模型跨受试者泛化能力。
MEASURE: Multi-scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Staging
- 设计多尺度架构，剥离冗余域相关特征，保留关键时频信息
- 在SleepEDF-20和MASS数据集上超越现有最优方法
- 适合需要强泛化性能的睡眠障碍诊断系统开发者
基于深度学习的自动睡眠分期已显著提升性能，在睡眠障碍诊断中发挥重要作用。然而，由于生理信号的个体差异，现有模型在未见受试者上泛化能力差，导致分布外性能下降。为解决此问题，领域泛化方法被研究以在训练中确保对未见领域的泛化性能。对比学习通过跨域对齐同类别样本，已被证明可学习域不变特征。但现有方法未能显式处理样本间非共享信息中嵌入的域特性，难以提取充分的域不变表示。本文认为，消除冗余的域相关特征——即过度域相关信息——是弥合域差距的关键。然而，直接抑制域相关属性常导致高层特征过拟合，限制其利用多层级中的丰富时序与频谱信息。为此，我们提出新型MEASURE（多尺度最小充分表示学习）框架，在有效减少域相关性的同时，保留用于睡眠分期的关键时序与频谱特征。在公开睡眠分期基准数据集SleepEDF-20和MASS上的大量实验表明，所提方法持续优于现有最先进方法。代码已开源：https://github.com/ku-milab/Measure
原文摘要 · Abstract (English)
Deep learning-based automatic sleep staging has significantly advanced in performance and plays a crucial role in the diagnosis of sleep disorders. However, those models often struggle to generalize on unseen subjects due to variability in physiological signals, resulting in degraded performance in out-of-distribution scenarios. To address this issue, domain generalization approaches have recently been studied to ensure generalized performance on unseen domains during training. Among those techniques, contrastive learning has proven its validity in learning domain-invariant features by aligning samples of the same class across different domains. Despite its potential, many existing methods are insufficient to extract adequately domain-invariant representations, as they do not explicitly address domain characteristics embedded within the unshared information across samples. In this paper, we posit that mitigating such domain-relevant attributes-referred to as excess domain-relevant information-is key to bridging the domain gap. However, the direct strategy to mitigate the domain-relevant attributes often overfits features at the high-level information, limiting their ability to leverage the diverse temporal and spectral information encoded in the multiple feature levels. To address these limitations, we propose a novel MEASURE (Multi-scalE minimAl SUfficient Representation lEarning) framework, which effectively reduces domain-relevant information while preserving essential temporal and spectral features for sleep stage classification. In our exhaustive experiments on publicly available sleep staging benchmark datasets, SleepEDF-20 and MASS, our proposed method consistently outperformed state-of-the-art methods. Our code is available at : https://github.com/ku-milab/Measure
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