EEG Automatic Sleep Stage Staging Algorithm Based on Spectral Band Fusion
WANG Qi
SHEN Yuhui
Abstract：Due to the unbalanced data set,the performance of sleep stage staging models based on deep learning is poor.In this paper,an automatic sleep staging algorithm based on spectral band fusion data enhancement is proposed to solve the above prob-lems and improve the performance of the model.In this algorithm,the time-frequency characteristics of EEG signals are extracted by convolutional neural network and bidirectional gated cyclic unit,and then the sleep stage is segmented.The EEG signals of 153 healthy subjects in the sleep EDF dataset are trained as the sample data of this model.The accuracy of this model is about 85.3%,and the Kappa value is 0.78.Compared with the traditional baseline model,the proposed sleep staging model based on band fusion has significantly improved accuracy and consistency.
Keywords：sleep stage stagingdata augmentationspectral band fusionconvolutional neural networkrecurrent neural network
Publication Date：2023-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 2859-2862,2983 )