LGSleepNet: An Automatic Sleep Staging Model Based on Local and Global Representation Learning

Sleep staging is an indispensable indicator for measuring sleep quality and evaluating sleep disorders. Deep learning methods have been successfully applied to automatic sleep staging (ASS) based on electroencephalogram (EEG) signals, achieving significant progress. Previous studies have, however, been limited to extracting local features of EEG signals while neglecting the importance of global features. To solve this problem, we propose a novel ASS model named LGSleepNet, which consists of an asymmetric Siamese neural network (ASNN), deep adaptive orthogonal fusion (DAOF) block, and weighted polynomial cross entropy (WPCE) loss function. Specifically, the ASNN is capable of simultaneously extracting local and global features from EEG signals, which provides diverse semantic representations for sleep staging; moreover, a DAOF block is proposed to eliminate the information redundancy and semantic deviation among heterogeneous features by orthogonalization and adaptive fusion, which strengthens the correlation representation between local and global features. Ultimately, a WPCE loss function is designed to improve the decision-making ability of the classification head and alleviate the problem of sample imbalance. We evaluate the LGSleepNet on three publicly available datasets, namely Sleep-EDF-20, Sleep-EDF-78, and SVUH-UCD, which achieves macro F1-scores of 80.7%, 76.0%, and 75.1% and overall accuracy of 86.0%, 82.3%, and 76.3%, respectively. The experimental results indicate that the LGSleepNet performs at an advanced level compared to other state-of-the-art methods.