AI RESEARCH

Position: Evaluation of ECG Representations Must Be Fixed

arXiv CS.AI

ArXi:2602.17531v2 Announce Type: replace-cross This position paper argues that current benchmarking practice in 12-lead ECG representation learning must be fixed to ensure progress is reliable and aligned with clinically meaningful objectives. The field has largely converged on three public multi-label benchmarks (PTB-XL, CPSC2018, CSN) dominated by arrhythmia and waveform-morphology labels, even though the ECG is known to encode substantially broader clinical information.