EEG Sequence Dependencies Learning With Fine-grained Sleep Transition Graph for Sleep Stage Classification

Sleep stage classification assists in evaluating sleep quality and diagnosing sleep disorders. Existing methods based on spatial-temporal graph convolutional networks (STGCNs) have shown excellent performance in recognizing sleep stages. However, current works learn transition dependencies in sleep sequence in a coarse manner and restrict the modeling of brain activity to a significantly brief period. To address the above issues, we propose a fine-grained sleep transition graph (FSTG) method to learn EEG sequence dependencies for sleep stage classification in this paper. Specifically, the novel sleep transition graph (STG) constructor (STG-C) is proposed to model sleep sequence transition dependencies in a more fine-grained manner, extracting effective auxiliary information from sleep contextual sequences for sleep staging. In the STG-C, we initially explore brain connectivity over extended sleep temporal scales, utilizing the graph fusion mechanism (GFM) to learn the multiple temporal correlations of brain channels. Furthermore, to better capture dependencies of sleep sequence, a skip-gated STGCN (SG-STGCN) is designed as the strong STG sequence learner. The proposed FSTG model achieves state-of-the-art performance in public sleep staging datasets, ISRUC-S3 and ISRUC-S1. Additionally, visualization experiments demonstrate the effectiveness and interpretability of FSTG in sleep staging classification.