# ECG Heart-beat Classification Using Multimodal Image Fusion

arXiv: https://arxiv.org/abs/2105.13536 · HTML: https://mlsanity.com/paper/2105.13536/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 3 of 3)

- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal](https://mlsanity.com/paper/2008.05503/) (2020)

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

- [A Survey on Physiological Signal Based Emotion Recognition](https://mlsanity.com/paper/2205.10466/) (2022)
- [Graph Neural Networks for Topological Feature Extraction in ECG Classification](https://mlsanity.com/paper/2311.04228/) (2023)
- [Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion](https://mlsanity.com/paper/2107.04566/) (2021)
- [ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2110.09983/) (2021)
- [Uncertainty-Aware Multi-view Arrhythmia Classification from ECG](https://mlsanity.com/paper/2506.06342/) (2025)

## Abstract (arXiv)

In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end to end deep learning. We perform experiments on PhysioNet MIT-BIH dataset for five different arrhythmias in accordance with the AAMI EC57 standard and on PTB diagnostics dataset for myocardial infarction (MI) classification. We achieved an state of an art results in terms of prediction accuracy, precision and recall.