Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration (AFR). The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features. The second module is the temporal context encoder (TCE) that leverages a multi-head attention mechanism to capture the temporal dependencies among the extracted features. Particularly, the multi-head attention deploys causal convolutions to model the temporal relations in the input features. We evaluate the performance of our proposed AttnSleep model using three public datasets. The results show that our AttnSleep outperforms state-of-the-art techniques in terms of different evaluation metrics. Our source codes, experimental data, and supplementary materials are available at https://github.com/emadeldeen24/AttnSleep .

中文翻译：

---

基于注意力的深度学习方法用于单通道脑电图的睡眠阶段分类

自动睡眠阶段mymargin分类对于衡量睡眠质量非常重要。在本文中，我们提出了一种新颖的基于注意力的深度学习架构，称为AttnSleep，用于使用单通道EEG信号对睡眠阶段进行分类。该体系结构从基于多分辨率卷积神经网络（MRCNN）和自适应特征重新校准（AFR）的特征提取模块开始。MRCNN可以提取低频和高频特征，而AFR可以通过对特征之间的相互依赖性进行建模来提高提取特征的质量。第二个模块是时间上下文编码器（TCE），它利用多头注意力机制来捕获所提取特征之间的时间相关性。特别，多头注意力会展开因果卷积以对输入要素中的时间关系进行建模。我们使用三个公共数据集评估了我们提出的AttnSleep模型的性能。结果表明，就不同的评估指标而言，我们的AttnSleep优于最新技术。我们的源代码，实验数据和补充材料可在以下位置获得：https://github.com/emadeldeen24/AttnSleep 。