DHFusion: Deep Hypergraph Convolutional Network for Multi-Modal Physiological Signals Fusion

Multi-modal physiological signals fusion integrates multiple heterogeneous physiological signals to characterize human physiological activities, which is basic research on biomedical signal processing. Most researches on multi-modal physiological signal fusion are based on heterogeneous graph fusion networks (HGFNs) while they ignore the high-order correlations between multi-modal physiological signals. Moreover, most HGFNs suffer from the over-smoothing problem, causing failures in deeper networks. To address these issues, this paper proposes a deep hypergraph convolutional network called DHFusion for multi-modal physiological signal fusion. Specially, DHFusion designs deep hypergraph convolution (DHGCN) layer to effectively integrate multiple physiological signals and obtain multi-modal features. DHFusion then introduces JK readout layer to enable multi-layer DHGCN to capture deep multi-modal features based on high-order correlations. DHFusion effectively solves the over-smoothing of HGFNs and performs well in representing deep multi-modal features. Experimental results over the state-of-the-art methods on two benchmark datasets demonstrate the effectiveness of the proposed method.