  Electrocardiography (ECG) is a non-invasive tool for predicting
cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show
promising performance owing to the rapid development of deep learning
techniques. However, the label scarcity problem, the co-occurrence of multiple
CVDs and the poor performance on unseen datasets greatly hinder the widespread
application of deep learning-based models. Addressing them in a unified
framework remains a significant challenge. To this end, we propose a
multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs
simultaneously with limited supervision. In the ECGMatch, an ECGAugment module
is developed for weak and strong ECG data augmentation, which generates diverse
samples for model training. Subsequently, a hyperparameter-efficient framework
with neighbor agreement modeling and knowledge distillation is designed for
pseudo-label generation and refinement, which mitigates the label scarcity
problem. Finally, a label correlation alignment module is proposed to capture
the co-occurrence information of different CVDs within labeled samples and
propagate this information to unlabeled samples. Extensive experiments on four
datasets and three protocols demonstrate the effectiveness and stability of the
proposed model, especially on unseen datasets. As such, this model can pave the
way for diagnostic systems that achieve robust performance on multi-label CVDs
prediction with limited supervision.
