睡眠分期是诊断和治疗睡眠相关疾病的重要步骤。目前，大多数监督学习模型都面临着标签数据不足的问题。此外，大多数睡眠分期模型都是基于多通道脑电图，模型过于复杂，不适合家庭睡眠监测场景。为了解决这些问题，本研究提出了一种基于伪标签优化和称为 SHNN 的单通道睡眠混合神经网络的睡眠分期方法。在 SHNN 模型中，我们设计了一个多尺度卷积神经网络 (CNN) 来从单通道 EEG 中提取特征，并使用双向循环门控单元 (Bi-GRU) 来获取睡眠数据序列的时间上下文信息. 基于单通道脑电图（FPz-Cz、Pz-Oz、k一个pp一个和 MF1 分数。此外，伪标签优化算法在其他睡眠分期方法上也能取得很好的效果。SHNN 代码可在 https://github.com/Caowenpeng/SHNN 获取。
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SHNN: A single-channel EEG sleep staging model based on semi-supervised learning
Sleep staging is an essential step in the diagnosis and treatment of sleep-related diseases. Currently, most supervised learning models face the problem of insufficient labeled data. In addition, most sleep staging models are based on multi-channel EEG, and the models are too complex to be suitable for home sleep monitoring scenarios. To tackle these problems, this study proposes a sleep staging method based on pseudo-label optimization and a single-channel sleep hybrid neural network called SHNN. In the SHNN model, we design a multi-scale convolutional neural network (CNN) to extract the features from the single-channel EEG and use a Bi-directional recurrent gating unit (Bi-GRU) to obtain temporal context information of sleep data sequences. Extensive experiments based on the single-channel EEG (FPz-Cz, Pz-Oz, and Cz-A1) of the Sleep-EDFx and the DREAMS-SUB datasets validate the effectiveness of the SHNN model and the pseudo-label optimization algorithm therein outperforming current single-channel methods regarding the accuracy, kappa, and MF1 Score. Moreover, the pseudo-label optimization algorithm can achieve good results on other sleep staging methods. The SHNN code is available at https://github.com/Caowenpeng/SHNN.