Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts
This work addresses the need to digitize paper ECGs for building diverse datasets and enabling automated cardiac disease analysis, though it is incremental as it adapts existing methods to a specific challenge.
The authors tackled the problem of reconstructing ECG signals from printouts by combining Hough transform and deep learning, achieving a signal-to-noise ratio of 17.02 in cross-validation and winning first place in the 2024 PhysioNet Challenge with a score of 12.15 on a hidden set.
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.