Abstract: Accurate sleep staging is an important basis for evaluating sleep quality and diagnosing related diseases.Aiming at the differences between electroencephalogram(EEG) andElectrooculogram in different stages of sleep,this paper proposes a new feature fusion deep network based on EEG and EOG,called MAFSNet,to realize automatic sleep staging.Specifically,we first design two different one-dimensional convolutional neural networks to extract effective sleep features from EEG and EOG signals.Se-condly,an adaptive feature fusion module is constructed to assign different weights to the features according to their contribution degree.By enhancing discriminant features and suppressing irrelevant features,an adaptive fusion feature containing multi-modal sleep information is obtained.Then,the time series related information in the sleep stage transition rule is learned using the bidirectional long short-term memory network.Finally,the public data set Sleep-EDF is used to verify the effectiveness of the proposed model to achieve five-stage sleep staging.The results show that the proposed method has high classification performance in sleep staging,with the accuracy of 94.1%,Cohen's Kappa coefficient of 88.2% and Macro-averaged F1-score of 81.9%,in which the recall rate of N1 and REM sleep stages is significantly increased to 64.6% and 93.5%,respectively.

Key words: Automatic sleep staging, EEG signal, EOG signal, Deep neural network, Adaptive feature fusion