# Self-Trained Model for ECG Complex Delineation

arXiv: https://arxiv.org/abs/2406.02711 · HTML: https://mlsanity.com/paper/2406.02711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [DENS-ECG: A Deep Learning Approach for ECG Signal Delineation](https://mlsanity.com/paper/2005.08689/) (2020)
- [Deep Learning for ECG Segmentation](https://mlsanity.com/paper/2001.04689/) (2020)
- [Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification](https://mlsanity.com/paper/2305.18592/) (2023)

## Abstract (arXiv)

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.