Key result
Novel self-trained pseudolabeling of unlabeled data improves ECG delineation prediction quality.
Why the study?
Existing methods for ECG delineation, including deep learning techniques, face limitations primarily related to dataset size and robustness.
A novel self-trained deep learning method using pseudolabeling on unlabeled data improves the accuracy of ECG complex delineation.
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.
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Avetisyan et al. (2025) studied ECG delineation. Self-trained method with pseudolabeling vs. Existing approaches was evaluated on Prediction quality of ECG delineation. A novel self-trained method using pseudolabeling of unlabeled ECG data improved the prediction quality of ECG delineation.
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