ADAST：基于迭代自训练的注意力跨域脑电睡眠分期框架
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
摘要：
睡眠分期在睡眠障碍的诊断和治疗中具有重要意义。最近，已经提出了许多数据驱动的深度学习模型用于自动睡眠分期。他们主要在一个大型的公共标签睡眠数据集上训练模型，并在一个较小的有兴趣对象的数据集上测试它。然而，他们通常假设训练和测试数据来自相同的分布，这在现实世界的场景中可能不成立。无监督域自适应（UDA）最近已经开发来处理这个域转移问题。然而，先前应用于睡眠分期的UDA方法具有两个主要限制。首先，它们依赖于用于域对齐的完全共享的模型，这可能在特征提取期间丢失特定于域的信息。其次，它们只在全局上对齐源和目标分布，而不考虑目标域中的类信息，这阻碍了模型在测试时的分类性能。在这项工作中，我们提出了一种新的对抗性学习框架ADAST来解决未标记目标域中的域转移问题。首先，我们开发了一个非共享的注意力机制，以保持在这两个领域的特定领域的功能。其次，我们设计了一种迭代的自训练策略，通过目标域伪标签来提高目标域的分类性能。我们还提出了双重不同的分类器，以增加伪标签的鲁棒性和质量。六个跨域场景的实验结果验证了我们提出的框架的有效性和它的优势，国家的最先进的UDA方法。源代码可在https://github.com/emadeldeen24/ADAST上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
