Exploiting Spatial-temporal Data for Sleep Stage Classification via
  Hypergraph Learning
Yuze Liu Ziming Zhao Tiehua Zhang Kang Wang Xin Chen Xiaowei Huang Jun Yin Zhishu Shen
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.