# Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Models

arXiv: https://arxiv.org/abs/2502.17887 · HTML: https://mlsanity.com/paper/2502.17887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [MedViT: A Robust Vision Transformer for Generalized Medical Image Classification](https://mlsanity.com/paper/2302.09462/) (2023)
- [Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1707.01836/) (2017)
- [ECG arrhythmia classification using a 2-D convolutional neural network](https://mlsanity.com/paper/1804.06812/) (2018)
- [Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram](https://mlsanity.com/paper/2010.10328/) (2020)
- [Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma](https://mlsanity.com/paper/2204.13838/) (2022)
- [Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals](https://mlsanity.com/paper/2211.03171/) (2022)

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

- [Explainable Deep Neural Network for Multimodal ECG Signals: Intermediate vs Late Fusion](https://mlsanity.com/paper/2508.11666/) (2025)

## Abstract (arXiv)

In Romania, cardiovascular problems are the leading cause of death, accounting for nearly one-third of annual fatalities. The severity of this situation calls for innovative diagnosis method for cardiovascular diseases. This article aims to explore efficient, light-weight and rapid methods for arrhythmia diagnosis, in resource-constrained healthcare settings. Due to the lack of Romanian public medical data, we trained our systems using international public datasets, having in mind that the ECG signals are the same regardless the patients' nationality. Within this purpose, we combined multiple datasets, usually used in the field of arrhythmias classification: PTB-XL electrocardiography dataset , PTB Diagnostic ECG Database, China 12-Lead ECG Challenge Database, Georgia 12-Lead ECG Challenge Database, and St. Petersburg INCART 12-lead Arrhythmia Database. For the input data, we employed ECG signal processing methods, specifically a variant of the Pan-Tompkins algorithm, useful in arrhythmia classification because it provides a robust and efficient method for detecting QRS complexes in ECG signals. Additionally, we used machine learning techniques, widely used for the task of classification, including convolutional neural networks (1D CNNs, 2D CNNs, ResNet) and Vision Transformers (ViTs). The systems were evaluated in terms of accuracy and F1 score. We annalysed our dataset from two perspectives. First, we fed the systems with the ECG signals and the GRU-based 1D CNN model achieved the highest accuracy of 93.4% among all the tested architectures. Secondly, we transformed ECG signals into images and the CNN2D model achieved an accuracy of 92.16%.