Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for modelling non-Euclidean data, are unable to consider the heterogeneity and interactivity of multimodal data as well as the spatial-temporal correlation simultaneously, which hinders a further improvement of classification performance. In this paper, we propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Hypergraphs can construct multi-modal/multi-type data instead of using simple pairwise between two subjects. STHL creates spatial and temporal hyperedges separately to build node correlations, then it conducts type-specific hypergraph learning process to encode the attributes into the embedding space. Extensive experiments show that our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks.

翻译：睡眠阶段分类对于检测患者的健康状况至关重要。现有模型主要利用卷积神经网络（CNN）处理欧几里得数据，以及图卷积网络（GNN）处理非欧几里得数据，但无法同时考虑多模态数据的异质性与交互性以及时空相关性，这阻碍了分类性能的进一步提升。本文提出了一种动态学习框架STHL，该框架引入超图对时空数据进行编码以实现睡眠阶段分类。超图能够构建多模态/多类型数据，而非仅在两个对象间使用简单的成对关系。STHL分别构建空间超边和时间超边以建立节点相关性，随后通过类型特定的超图学习过程将属性编码到嵌入空间中。大量实验表明，我们提出的STHL在睡眠阶段分类任务中优于当前最先进的模型。