  The classification of sleep stages plays a crucial role in understanding and
diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual
inspection by an expert that is time consuming and subjective procedure.
Recently, deep learning neural network approaches have been leveraged to
develop a generalized automated sleep staging and account for shifts in
distributions 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 the
sequential connections between temporally adjacent sleep epochs. To address
these issues, this work proposes an adaptive product graph learning-based graph
convolutional network, named ProductGraphSleepNet, for learning joint
spatio-temporal graphs along with a bidirectional gated recurrent unit and a
modified graph attention network to capture the attentive dynamics of sleep
stage transitions. Evaluation on two public databases: the Montreal Archive of
Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night
polysomnography 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 database
respectively). More importantly, the proposed network makes it possible for
clinicians to comprehend and interpret the learned connectivity graphs for
sleep stages.
