READ-ECG: ECG Printout Digitisation via Structured Visual Sequence Regression for Cardiovascular Analysis

Electrocardiograms (ECGs) are a primary cardiovascular diagnostic modality, but recordings available only as scanned or photographed printouts cannot be directly processed by signal-based AI tools. Direct image analysis entangles signal extraction with clinical interpretation, while multi-stage digitisation pipelines rely on brittle segmentation steps. We formulate ECG printout digitisation as a structured visual sequence regression problem within a decoupled Image → Signal → Analysis pipeline. We introduce READ-ECG (Reconstruction and Extraction via Attention-based Decoding of ECGs), an end-to-end framework that bypasses intermediate trace segmentation, employing a topology-aware attention-based decoder to directly regress spatial features into continuous multi-lead waveforms. To support future quality-control workflows, we incorporate aleatoric uncertainty modelling to identify potentially unreliable reconstructions. Evaluated across diverse benchmarks, READ-ECG achieves high-fidelity reconstruction on the combined PhysioNet evaluation (PCC: 0.998, SNR: 28.83 dB) and can adapt to severe real-world visual artefacts using only 81 real training images. Across three PTB-XL classification task families, a residual 1D CNN trained on original signals retains a mean AUROC of 0.927 on READ-ECG reconstructions. Evaluation with two pre-trained ECG foundation models further suggests that the digitised signals retain diagnostic information. By decoupling signal “reading” from “interpretation”, READ-ECG provides reusable waveforms to the signal-based cardiac AI ecosystem.