PedECG: Adult-to-Pediatric ECG Transfer Through Representation Reuse, Target Reinterpretation, and Evidence Validation

Pediatric ECG interpretation is inherently development-dependent: the diagnostic significance of heart rate, conduction intervals, voltage, axis, and repolarization changes throughout childhood. Although large adult ECG datasets provide rich cardiac knowledge, directly transferring adult models to children can also transfer diagnostic rules calibrated to adult physiology. To address this challenge, we propose PedECG, a decomposable framework for adult-to-pediatric ECG transfer that separates representation reuse, target-side reinterpretation, and record-specific evidence validation. PedECG preserves adult pretrained waveform representations with a frozen encoder, reinterprets them using quantitative ECG measurements and age-indexed pediatric reference context, and verifies candidate diagnoses against evidence from the current ECG record. Experiments show that adult cardiac representations remain highly reusable in the pediatric domain, while structured target-side reinterpretation and record-specific verification further improve how this transferred knowledge is converted into pediatric predictions; the learned interpretation also adapts to changes in the pediatric physiological reference frame. By separating reusable physiological knowledge from population-specific diagnostic interpretation, PedECG offers a general strategy for extending medical AI from data-rich source populations to clinically distinct target populations, helping make large-scale learned medical knowledge usable beyond the cohorts on which it was originally developed.