# Comparing Deep Neural Network for Multi-Label ECG Diagnosis From Scanned ECG

arXiv: https://arxiv.org/abs/2502.14909 · HTML: https://mlsanity.com/paper/2502.14909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization](https://mlsanity.com/paper/2307.01946/) (2023)
- [MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosis](https://mlsanity.com/paper/2311.04224/) (2023)
- [Transfer Learning in ECG Diagnosis: Is It Effective?](https://mlsanity.com/paper/2402.02021/) (2024)

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

- [HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding](https://mlsanity.com/paper/2506.05831/) (2025)

## Abstract (arXiv)

Automated ECG diagnosis has seen significant advancements with deep learning techniques, but real-world applications still face challenges when dealing with scanned paper ECGs. In this study, we explore multi-label classification of ECGs extracted from scanned images, moving beyond traditional binary classification (normal/abnormal). We evaluate the performance of multiple deep neural network architectures, including AlexNet, VGG, ResNet, and Vision Transformer, on scanned ECG datasets. Our comparative analysis examines model accuracy, robustness to image artifacts, and generalizability across different ECG conditions. Additionally, we investigate whether ECG signals extracted from scanned images retain sufficient diagnostic information for reliable automated classification. The findings highlight the strengths and limitations of each architecture, providing insights into the feasibility of image-based ECG diagnosis and its potential integration into clinical workflows.