Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
This addresses the problem of improving classification accuracy for sleep stage analysis, which is crucial for patient health monitoring, but it appears incremental as it builds on existing hypergraph and GNN methods.
The paper tackled sleep stage classification by proposing STHL, a dynamic hypergraph learning framework that encodes spatial-temporal data, and it outperformed state-of-the-art models in experiments.
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.