Self-Supervised JEPA Pretraining for ECG Representations with Aligned Clinical Reports
Year: 2026
Type: preprint
Abstract: Labeled electrocardiograms (ECG) are scarce, while unlabeled recordings are abundant. We pretrain a joint-embedding predictive architecture (JEPA) on unlabeled ECG segments by predicting the embeddings of masked leads and segments, and we align the resulting representations with clinical report text. We evaluate on a public 12-lead benchmark with a linear probe and a low-label regime, and on synthetic ECG with controlled anomalies. This is a research protocol, not a clinical tool. A synthetic pilot is too easy to discriminate, so the pretraining results are pending. Preprint. Results in this version come from small-scale pilot experiments; the full experimental protocol is described in the text and is pending.
Author: Santos, Dheiver Francisco
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225096004