MaskedGAFNet: Self-Supervised Representation Learning for 12-lead ECG using GADF and Masked Image Modeling

Myocardial infarction (MI) remains a major cause of mortality worldwide, requiring rapid and accurate localization of infarct regions. While 12-lead ECGs are the clinical standard for MI diagnosis, interpretation is often expertise-dependent and inconsistent. To address this, we propose a novel self-supervised learning framework based on Gramian Angular Difference Field (GADF) representation, which converts 1D ECG signals into 2D images that capture temporal and morphological patterns. This enhances robustness to noise and aligns with clinical visual interpretation.