BTCRSleep: a boundary temporal context refinement based fully convolutional network for sleep staging with single-channel EEG.

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Abstract

Sleep staging studies on single-channel EEG mainly exploit deep learning methods combining convolutional neural networks and recurrent neural networks. However, when typical brain waves (such as K-complexes or sleep spindles) that identify sleep stages span two epochs, the abstract process of CNN extracting features from each sleep stage may cause the loss of boundary context information. This study attempts to capture the boundary context for improving the performance of sleep staging, which contains the characteristics of brain waves during sleep stage transition.In this paper, we propose a fully convolutional network
with boundary temporal context refinement, called BTCRSleep (Boundary Temporal Context Refinement Sleep). Boundary temporal context refinement module refines the boundary information of sleep stages on the basis of extracting multi-scale temporal dependencies between epochs and enhance the abstract capability of the boundary temporal context. In addition, we design a class-aware data augmentation method to effectively learn the boundary temporal context between minority class and other sleep stages.We evaluate the performance of our proposed network using four public datasets, the 2013 version of Sleep-EDF Expanded (SEDF), the 2018 version of Sleep-EDF Expanded (SEDFX), Sleep Heart Health Study (SHHS) and CAP Sleep Database(CAP). The evaluation results on four datasets showed that our model obtains the best total accuracy and kappa score compared to the state-of-the-art methods. On average, accuracy of 84.9% in SEDF, 82.9% in SEDFX, 85.2% in SHHS and 76.9% in CAP are obtained under subject-independent cross validation.We demonstrate that boundary temporal context contributes to the improvement in capturing the temporal dependencies across different epochs.© 2023 Institute of Physics and Engineering in Medicine.