A Deep Learning Pipeline Using Synthetic Data to Improve Interpretation of Paper ECG Images

Cardiovascular diseases (CVDs) are the leading global cause of death, and early detection is essential to improve patient outcomes. Electrocardiograms (ECGs), especially 12-lead ECGs, play a key role in the identification of CVDs. These are routinely interpreted by human experts, a process that is time-consuming and requires expert knowledge. Historical research in this area has focused on automatic ECG interpretation from digital signals, with recent deep learning approaches achieving strong results. In practice, however, most ECG data in clinical practice are stored or shared in image form. To bridge this gap, we propose a deep learning framework designed specifically to classify paper-like ECG images into five main diagnostic categories. Our method was the winning entry to the 2024 British Heart Foundation Open Data Science Challenge. It addresses two main challenges of paper ECG classification: visual noise (e.g., shadows or creases) and the need to detect fine-detailed waveform patterns. We propose a pre-processing pipeline that reduces visual noise and a two-stage fine-tuning strategy: the model is first fine-tuned on synthetic and external ECG image datasets to learn domain-specific features, and then further fine-tuned on the target dataset to enhance disease-specific recognition. We adopt the ConvNeXt architecture as the backbone of our model. Our method achieved AUROC scores of 0.9688 on the public validation set and 0.9677 on the private test set of the British Heart Foundation Open Data Science Challenge, highlighting its potential as a practical tool for automated ECG interpretation in clinical workflows.