# Graph Neural Networks for Topological Feature Extraction in ECG Classification

arXiv: https://arxiv.org/abs/2311.04228 · HTML: https://mlsanity.com/paper/2311.04228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [ECG Heart-beat Classification Using Multimodal Image Fusion](https://mlsanity.com/paper/2105.13536/) (2021)

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

- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics](https://mlsanity.com/paper/2608.03690/) (2026)
- [BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment](https://mlsanity.com/paper/2609.31546/) (2026)

## Abstract (arXiv)

The electrocardiogram (ECG) is a dependable instrument for assessing the function of the cardiovascular system. There has recently been much emphasis on precisely classifying ECGs. While ECG situations have numerous similarities, little attention has been paid to categorizing ECGs using graph neural networks. In this study, we offer three distinct techniques for classifying heartbeats using deep graph neural networks to classify the ECG signals accurately. We suggest using different methods to extract topological features from the ECG signal and then using a branch of the graph neural network named graph isomorphism network for classifying the ECGs. On the PTB Diagnostics data set, we tested the three proposed techniques. According to the findings, the three proposed techniques are capable of making arrhythmia classification predictions with the accuracy of 99.38, 98.76, and 91.93 percent, respectively.