# Deep learning based ECG segmentation for delineation of diverse arrhythmias

arXiv: https://arxiv.org/abs/2304.06237 · HTML: https://mlsanity.com/paper/2304.06237/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [Deep Learning for ECG Segmentation](https://mlsanity.com/paper/2001.04689/) (2020)
- [ECG Segmentation by Neural Networks: Errors and Correction](https://mlsanity.com/paper/1812.10386/) (2018)
- [Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing](https://mlsanity.com/paper/2204.04420/) (2022)
- [Finding morphology points of electrocardiographic signal waves using wavelet analysis](https://mlsanity.com/paper/1808.04804/) (2018)

## Built on by (most cited first, 7 of 7)

- [Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2410.08559/) (2024)
- [ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation](https://mlsanity.com/paper/2603.14326/) (2026)
- [SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation](https://mlsanity.com/paper/2507.18323/) (2025)
- [Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis](https://mlsanity.com/paper/2607.01145/) (2026)
- [Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation](https://mlsanity.com/paper/2605.15722/) (2026)
- [Automated ECG Interval Measurement and Wave Delineation Using Fast Fourier Convolution ResNet](https://mlsanity.com/paper/2608.00058/) (2026)
- [Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations](https://mlsanity.com/paper/2608.09053/) (2026)

## Abstract (arXiv)

Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy.