# Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts

arXiv: https://arxiv.org/abs/2410.14185 · HTML: https://mlsanity.com/paper/2410.14185/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization](https://mlsanity.com/paper/2307.01946/) (2023)

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

- [Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research](https://mlsanity.com/paper/2510.19590/) (2025)
- [Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images](https://mlsanity.com/paper/2508.09165/) (2025)
- [VLM-in-the-Loop: A Plug-In Quality Assurance Module for ECG Digitization Pipelines](https://mlsanity.com/paper/2604.00396/) (2026)
- [Learning ECG Image Representations via Dual Physiological-Aware Alignments](https://mlsanity.com/paper/2604.01526/) (2026)
- [ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening](https://mlsanity.com/paper/2607.07683/) (2026)

## Abstract (arXiv)

This work presents our team's (SignalSavants) winning contribution to the 2024 George B. Moody PhysioNet Challenge. The Challenge had two goals: reconstruct ECG signals from printouts and classify them for cardiac diseases. Our focus was the first task. Despite many ECGs being digitally recorded today, paper ECGs remain common throughout the world. Digitising them could help build more diverse datasets and enable automated analyses. However, the presence of varying recording standards and poor image quality requires a data-centric approach for developing robust models that can generalise effectively. Our approach combines the creation of a diverse training set, Hough transform to rotate images, a U-Net based segmentation model to identify individual signals, and mask vectorisation to reconstruct the signals. We assessed the performance of our models using the 10-fold stratified cross-validation (CV) split of 21,799 recordings proposed by the PTB-XL dataset. On the digitisation task, our model achieved an average CV signal-to-noise ratio of 17.02 and an official Challenge score of 12.15 on the hidden set, securing first place in the competition. Our study shows the challenges of building robust, generalisable, digitisation approaches. Such models require large amounts of resources (data, time, and computational power) but have great potential in diversifying the data available.