生物医学工程研究2025，Vol.44Issue(1)：24-30,7.DOI:10.19529/j.cnki.1672-6278.2025.01.04
基于多模态融合策略和注意力机制的睡眠自动分期模型
Automatic sleep staging model based on multimodal fusion strategy and attention mechanism
摘要
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.关键词
睡眠分期/多模态信号/深度学习/特征融合/编码器/分类网络Key words
Sleep staging/Multimodal signals/Deep learning/Feature fusion/Encoder/Classified networks分类
基础医学引用本文复制引用
陈丽娟,王磊,沙宪政,常世杰,陈勇..基于多模态融合策略和注意力机制的睡眠自动分期模型[J].生物医学工程研究,2025,44(1):24-30,7.基金项目
南通市卫生健康委员会科研项目(QNZ2022006). （QNZ2022006）
