Automatic sleep staging based on deep learning (DL) has been attracting attention for analyzing sleep quality and determining treatment effects. It is challenging to acquire long-term sleep data from numerous subjects and manually labeling them even though most DL-based models are trained using large-scale sleep data to provide state-of-the-art performance. One way to overcome this data shortage is to create a pre-trained network with an existing large-scale dataset (source domain) that is applicable to small cohorts of datasets (target domain); however, discrepancies in data distribution between the domains prevent successful refinement of this approach. In this paper, we propose an unsupervised domain adaptation method for sleep staging networks to reduce discrepancies by re-aligning the domains in the same space and producing domain-invariant features. Specifically, in addition to a classical domain discriminator, we introduce local discriminators - subject and stage - to maintain the intrinsic structure of sleep data to decrease local misalignments while using adversarial learning to play a minimax game between the feature extractor and discriminators. Moreover, we present several optimization schemes during training because the conventional adversarial learning is not effective to our training scheme. We evaluate the performance of the proposed method by examining the staging performances of a baseline network compared with direct transfer (DT) learning in various conditions. The experimental results demonstrate that the proposed domain adaptation significantly improves the performance though it needs no labeled sleep data in target domain.

中文翻译：

---

在睡眠分期网络的无监督域适应中传输结构化知识


基于深度学习（DL）的自动睡眠分期在分析睡眠质量和确定治疗效果方面引起了人们的关注。尽管大多数基于深度学习的模型都是使用大规模睡眠数据进行训练以提供最先进的性能，但从众多受试者中获取长期睡眠数据并手动标记它们仍然具有挑战性。克服这种数据短缺的一种方法是使用现有的大型数据集（源域）创建一个预训练的网络，该网络适用于小型数据集（目标域）；然而，域之间数据分布的差异阻碍了这种方法的成功改进。在本文中，我们提出了一种用于睡眠分期网络的无监督域适应方法，通过在同一空间中重新对齐域并产生域不变特征来减少差异。具体来说，除了经典的域鉴别器之外，我们还引入了局部鉴别器（主题和阶段）来维护睡眠数据的内在结构，以减少局部错位，同时使用对抗性学习在特征提取器和鉴别器之间玩极小极大游戏。此外，我们在训练期间提出了几种优化方案，因为传统的对抗性学习对我们的训练方案无效。我们通过检查基线网络与各种条件下的直接迁移（DT）学习的分期性能来评估所提出方法的性能。实验结果表明，所提出的域适应虽然不需要目标域中的标记睡眠数据，但显着提高了性能。