# GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention

arXiv: https://arxiv.org/abs/2501.01960 · HTML: https://mlsanity.com/paper/2501.01960/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [Learning Domain-Invariant Representations for Cross-Domain Image Registration via Scene-Appearance Disentanglement](https://mlsanity.com/paper/2601.08875/) (2026)
- [PCReg-Net: Progressive Contrast-Guided Registration for Cross-Domain Image Alignment](https://mlsanity.com/paper/2602.13304/) (2026)

## Abstract (arXiv)

Electrocardiogram (ECG) analysis plays a crucial role in diagnosing cardiovascular diseases, but accurate interpretation of these complex signals remains challenging. This paper introduces a novel multimodal framework(GAF-FusionNet) for ECG classification that integrates time-series analysis with image-based representation using Gramian Angular Fields (GAF). Our approach employs a dual-layer cross-channel split attention module to adaptively fuse temporal and spatial features, enabling nuanced integration of complementary information. We evaluate GAF-FusionNet on three diverse ECG datasets: ECG200, ECG5000, and the MIT-BIH Arrhythmia Database. Results demonstrate significant improvements over state-of-the-art methods, with our model achieving 94.5%, 96.9%, and 99.6% accuracy on the respective datasets. Our code will soon be available at https://github.com/Cross-Innovation-Lab/GAF-FusionNet.git.