Abstract

Background The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers.

Methods the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations.

Results Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging—typical health monitoring tasks—demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning <3 % of the entire model’s data, substantially fortifying the model’s resilience to view missing while circumventing the need for complete retraining.

Conclusions The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring.

Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals

Recommended citation: Hu Qihan, Daomiao Wang, Hong Wu, Liu Jian, and Cuiwei Yang. "Efficient multi-view fusion and flexible adaptation to view missing in cardiovascular system signals." Neural Networks 181 (2025): 106760.
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