Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets. Inspired by existing domain generalization methods, we adopt the feature alignment idea and propose a framework called SleepDG to solve it. Considering both of local salient features and sequential features are important for sleep staging, we propose a Multi-level Feature Alignment combining epoch-level and sequence-level feature alignment to learn domain-invariant feature representations. Specifically, we design an Epoch-level Feature Alignment to align the feature distribution of each single sleep epoch among different domains, and a Sequence-level Feature Alignment to minimize the discrepancy of sequential features among different domains. SleepDG is validated on five public datasets, achieving the state-of-the-art performance.

翻译：自动睡眠分期对于睡眠评估与疾病诊断至关重要。现有方法大多依赖特定数据集，且因训练数据与测试数据同源而难以泛化至未见过的数据集。本文首次将域泛化引入自动睡眠分期，提出可泛化睡眠分期任务，旨在提升模型对未见过数据集的泛化能力。受现有域泛化方法启发，我们采用特征对齐思想并提出SleepDG框架解决该问题。鉴于局部显著特征与序列特征对睡眠分期同等重要，我们提出融合时期级与序列级特征对齐的多层级特征对齐方法，以学习域不变特征表示。具体而言，我们设计时期级特征对齐模块对齐不同域间单个睡眠时段的特征分布，同时构建序列级特征对齐模块减小不同域间序列特征的差异。在五个公开数据集上的实验表明，SleepDG取得了最先进的性能。