Self-Trained Model for ECG Complex Delineation
This work addresses dataset limitations for cardiologists in ECG diagnosis, but it is incremental as it builds on existing deep learning methods.
The authors tackled the problem of limited dataset size and robustness in ECG delineation by introducing a new dataset and a self-trained method using pseudolabeling on unlabeled ECG data, resulting in improved prediction quality.
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.