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The classification of sleep stages plays a crucial role in understanding anddiagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visualinspection by an expert that is time consuming and subjective procedure.Recently, deep learning neural network approaches have been leveraged todevelop a generalized automated sleep staging and account for shifts indistributions that may be caused by inherent inter/intra-subject variability,heterogeneity across datasets, and different recording environments. However,these networks ignore the connections among brain regions, and disregard thesequential connections between temporally adjacent sleep epochs. To addressthese issues, this work proposes an adaptive product graph learning-based graphconvolutional network, named ProductGraphSleepNet, for learning jointspatio-temporal graphs along with a bidirectional gated recurrent unit and amodified graph attention network to capture the attentive dynamics of sleepstage transitions. Evaluation on two public databases: the Montreal Archive ofSleep Studies (MASS) SS3; and the SleepEDF, which contain full nightpolysomnography recordings of 62 and 20 healthy subjects, respectively,demonstrates performance comparable to the state-of-the-art (Accuracy:0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each databaserespectively). More importantly, the proposed network makes it possible forclinicians to comprehend and interpret the learned connectivity graphs forsleep stages.

ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation