Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a large public labeled sleep dataset and test it on a smaller one with subjects of interest. However, they usually assume that the train and test data are drawn from the same distribution, which may not hold in real-world scenarios. Unsupervised domain adaption (UDA) has been recently developed to handle this domain shift problem. However, previous UDA methods applied for sleep staging have two main limitations. First, they rely on a totally shared model for the domain alignment, which may lose the domain-specific information during feature extraction. Second, they only align the source and target distributions globally without considering the class information in the target domain, which hinders the classification performance of the model while testing. In this work, we propose a novel adversarial learning framework called ADAST to tackle the domain shift problem in the unlabeled target domain. First, we develop an unshared attention mechanism to preserve the domain-specific features in both domains. Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels. We also propose dual distinct classifiers to increase the robustness and quality of the pseudo labels. The experimental results on six cross-domain scenarios validate the efficacy of our proposed framework and its advantage over state-of-the-art UDA methods. The source code is available at https://github.com/emadeldeen24/ADAST.
翻译:在诊断和治疗睡眠失常方面,睡眠变迁非常重要。最近,许多数据驱动的深层学习模型被推荐用于自动睡眠变迁。它们主要在大型公众标签的睡眠数据集上对模型进行培训,并将模型测试在较小的模型上,有感兴趣的主题。然而,它们通常假定火车和测试数据来自同一分布,在现实世界的情景中可能无法维持。最近开发了不受监督的域适应(UDA)以解决这个域变换问题。然而,以前用于睡眠变换的UDA方法有两个主要限制。首先,它们依赖于完全共享的域对齐模式,在功能提取过程中可能会丢失域特定信息。第二,它们只是将源和目标分布与目标域的类别信息进行对齐,而不考虑目标域的类别信息,从而妨碍模型的分类性能。在这项工作中,我们提议了一个新的对抗性对域域调框架(UDADAST)来应对未加标记的目标变换问题。首先,我们开发了一个无法共享的源关注机制来保护两个域域的特定特性。第二,我们设计了一个迭代自定义的自我升级的自我升级战略,目的是改进了双重目标域域域的自我升级标签。