SAGSleepNet: A deep learning model for sleep staging based on self-attention graph of polysomnography

Abstract

Sleep is crucial for human health. Automatic sleep stage classification based on polysomnography (PSG) is meaningful for the diagnosis of sleep diseases, which has attracted extensive attention recently. Most existed works learn the representations of PSG based on the conventional grid data structure, ignoring the topological channel correlations among different body regions in non-Euclidean space. To this end, we propose a deep learning model named SAGSleepNet, which transforms PSG into a robust graph structure by neural networks and self-attention mechanism, utilizing graph convolution network (GCN) and bidirectional gated recurrent unit (BiGRU) to capture epoch-level topological channel correlations and sequence-level temporal sleep transitions of PSG, respectively. The final learned representation is fed into a softmax layer to train an end-to-end model for automatic sleep staging. Experimental results on three public datasets (i.e., SleepEDFx, UCD, CAP) show that SAGSleepNet obtains the best classification performance compared with several baselines, including accuracy of 0.807 ,0.765, and 0.746, F1-score of 0.744, 0.750, and 0.695, and Cohen’s Kappa (κ) of 0.721, 0.690, and 0.660. In general, our work provides a reasonable graph structure to model PSG epoch, and makes contribution to explore implicit channel-wise information of PSG by deep learning techniques.