# ECGformer: Leveraging transformer for ECG heartbeat arrhythmia classification

arXiv: https://arxiv.org/abs/2401.05434 · HTML: https://mlsanity.com/paper/2401.05434/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 34 references that are in the map.
- First posted on arXiv 6 Jan 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)

## Built on by (most cited first, 5 of 5)

- [HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis](https://mlsanity.com/paper/2411.11896/) (2024)
- [Advancing Few-Shot Pediatric Arrhythmia Classification with a Novel Contrastive Loss and Multimodal Learning](https://mlsanity.com/paper/2509.19315/) (2025)
- [IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability](https://mlsanity.com/paper/2505.07533/) (2025)
- [Label-Conditioned Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Curriculum-Gated Contrastive Alignment](https://mlsanity.com/paper/2605.00647/) (2026)
- [Knowledge-Guided Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Label-Conditioned Contrastive Alignment](https://mlsanity.com/paper/2607.15928/) (2026)

## Abstract (arXiv)

An arrhythmia, also known as a dysrhythmia, refers to an irregular heartbeat. There are various types of arrhythmias that can originate from different areas of the heart, resulting in either a rapid, slow, or irregular heartbeat. An electrocardiogram (ECG) is a vital diagnostic tool used to detect heart irregularities and abnormalities, allowing experts to analyze the heart's electrical signals to identify intricate patterns and deviations from the norm. Over the past few decades, numerous studies have been conducted to develop automated methods for classifying heartbeats based on ECG data. In recent years, deep learning has demonstrated exceptional capabilities in tackling various medical challenges, particularly with transformers as a model architecture for sequence processing. By leveraging the transformers, we developed the ECGformer model for the classification of various arrhythmias present in electrocardiogram data. We assessed the suggested approach using the MIT-BIH and PTB datasets. ECG heartbeat arrhythmia classification results show that the proposed method is highly effective.