Automatic sleep staging model based on multimodal fusion strategy and attention mechanism
CHEN Lijuan
WANG Lei
SHA Xianzheng
CHANG Shijie
CHEN Yong
Abstract：To solve the problem that existing studies on sleep staging only focus on single channel electroencephalogram(EEG)da-ta,cannot effectively use sleep state transition rules,we proposed an automatic sleep staging model based on multimodal feature fusion and attention mechanism.Firstly,the representation learning module was constructed to capture the characteristics of multimodal sleep signals and explore the relationships between feature channels.Subsequently,a multichannel fusion strategy was designed to enhance the calibration learning of features and integrate complementary sleep information from multimodal signals.Finally,the fused features were input into the context channel dependency learning module,where attention mechanism was utilized to learn the contextual rela-tionships within sleep signals,to achieve precise sleep staging outcomes.The results showed that the accuracy of this model on the three public datasets Sleep-EDF-20,Sleep-EDF-78 and Montreal arohive of sleep studies(MASS)was 85.9%,85.2%and 88.5%,re-spectively,and the macro average F1 score(MF1)was 80.8%,80.0%and 82.1%,respectively.The accuracy and robustness of this model are superior to the other models,which can provide technical reference for sleep staging.
Keywords：Sleep stagingMultimodal signalsDeep learningFeature fusionEncoderClassified networks
Publication Date：2025-02-28
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：7( 24-30 )