CASleepNet: A Cross Attention-based multimodal fusion approach for sleep staging with EEG and EOG

Abstract

Automatic sleep staging is crucial for sleep assessment and diagnosis. Signals of different modalities, such as electroencephalogram (EEG) and electrooculogram (EOG), are of crucial importance for sleep staging. Therefore, effective fusion of different modal signals is the key to improve sleep staging performance. Current methods usually ignore the inter-modal interactions in the process of multimodal signals fusion, making it difficult to achieve optimal sleep staging performance. In order to solve this problem, we propose a sleep staging model, referred to as CASleepNet, which can extract the heterogeneous features of each modality through Temporal Context Module (TCM), and learn the intrinsic connection between different modal signals by using Dual Cross Attention Fusion (DCAF). Experimental results show that CASleepNet can effectively fuse multimodal information to improve the performance of sleep staging.