  Sleep stage recognition is crucial for assessing sleep and diagnosing chronic
diseases. Deep learning models, such as Convolutional Neural Networks and
Recurrent Neural Networks, are trained using grid data as input, making them
not capable of learning relationships in non-Euclidean spaces. Graph-based deep
models have been developed to address this issue when investigating the
external relationship of electrode signals across different brain regions.
However, the models cannot solve problems related to the internal relationships
between segments of electrode signals within a specific brain region. In this
study, we propose a Pearson correlation-based graph attention network, called
PearNet, as a solution to this problem. Graph nodes are generated based on the
spatial-temporal features extracted by a hierarchical feature extraction
method, and then the graph structure is learned adaptively to build node
connections. Based on our experiments on the Sleep-EDF-20 and Sleep-EDF-78
datasets, PearNet performs better than the state-of-the-art baselines.
