  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. Thus, 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. 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 less than 3% of the entire model's
data, substantially fortifying the model's resilience to view missing while
circumventing the need for complete retraining. The results demonstrate the
effectiveness of our approaches, highlighting their potential for practical
applications in cardiovascular health monitoring. Codes and models are released
at URL.
