# ECG Segmentation by Neural Networks: Errors and Correction

arXiv: https://arxiv.org/abs/1812.10386 · HTML: https://mlsanity.com/paper/1812.10386/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2410.08559/) (2024)
- [Deep learning based ECG segmentation for delineation of diverse arrhythmias](https://mlsanity.com/paper/2304.06237/) (2023)
- [Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation](https://mlsanity.com/paper/2111.12996/) (2021)

## Abstract (arXiv)

In this study we examined the question of how error correction occurs in an ensemble of deep convolutional networks, trained for an important applied problem: segmentation of Electrocardiograms(ECG). We also explore the possibility of using the information about ensemble errors to evaluate a quality of data representation, built by the network. This possibility arises from the effect of distillation of outliers, which was demonstarted for the ensemble, described in this paper.