Self-supervised foundation model for 12-lead ECGs, pretrained on 9.1 million recordings covering 164 cardiovascular conditions across adult and pediatric cohorts, including single-lead settings. Uses a HuBERT-style Transformer encoder and can be fine-tuned with a simple output layer for diagnosis and event-prediction tasks. Released in small, base, and large (~183M parameter) configurations by researchers at the University of Brescia.

Self-supervised ECG foundation model that adapts to varying lead combinations by patchifying 12-lead recordings across both space (leads) and time, then pretraining a ViT-B/75 encoder-decoder with a masked-autoencoder objective. Published at ICLR 2024 by VUNO Inc., and pretrained on the Chapman-Shaoxing-Ningbo dataset along with several other public 12-lead sources.

BEiT-base Vision Transformer that embeds images of standard 12-lead ECG printouts into a representation space, enabling zero-shot screening for structural heart disease by comparing a new ECG against reference case/control embedding centroids rather than requiring task-specific training. Trained on private Yale New Haven Health System ECG images and validated against the public EchoNext dataset. Aimed at scanned or legacy ECG images still common in EHR systems. Developed by Yale's CarDS Lab.