Electrocardiogram (ECG) delineation plays a crucial role in assisting
cardiologists with accurate diagnoses. Prior research studies have explored
various methods, including the application of deep learning techniques, to
achieve precise delineation. However, existing approaches face limitations
primarily related to dataset size and robustness. In this paper, we introduce a
dataset for ECG delineation and propose a novel self-trained method aimed at
leveraging a vast amount of unlabeled ECG data. Our approach involves the
pseudolabeling of unlabeled data using a neural network trained on our dataset.
Subsequently, we train the model on the newly labeled samples to enhance the
quality of delineation. We conduct experiments demonstrating that our dataset
is a valuable resource for training robust models and that our proposed
self-trained method improves the prediction quality of ECG delineation.

本文介绍了一种针对心电图（ECG）划分的新方法，通过伪标记未标记的数据并使用我们的数据集进行训练，从而改善 ECG 划分的预测质量，揭示了该数据集对训练稳健模型的价值。