# Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

arXiv: https://arxiv.org/abs/2410.10758 · HTML: https://mlsanity.com/paper/2410.10758/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)

## Built on by (most cited first, 1 of 1)

- [BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment](https://mlsanity.com/paper/2609.31546/) (2026)

## Abstract (arXiv)

With the advancements in graph neural network, there has been increasing interest in applying this network to ECG signal analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted features and applied a graph neural network to classify arrhythmias. The proposed model was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that this method can be considered an approach for arrhythmia classification.