Multimodal cardiac-sensing foundation model pretrained with generative masked pretraining on ECG, PPG, and paired clinical/machine-generated text reports from roughly 1.7 million individuals across three large-scale critical-care and outpatient ECG datasets. A channel-embedding scheme lets the same model accept any combination of 12-lead ECG, single-lead/wearable ECG, and PPG. The resulting embeddings transfer to diagnostic classification, demographic recognition, vital-sign measurement, clinical-outcome prediction, and ECG question answering. Pretrained weights require a signed academic-access agreement rather than an open download.

Self-supervised ECG representation-learning method that adapts Joint-Embedding Predictive Architecture (JEPA) -- originally developed for images -- to 1D electrocardiogram signals. A Vision Transformer encoder (ViT-XS/S/B) is pretrained to predict masked temporal segments of the ECG directly in latent feature space, using a masking strategy tailored to time-series, on more than 1 million ECGs pooled from MIMIC-IV-ECG, CODE-15%, PTB-XL, Chapman-Shaoxing, CPSC2018/Extra, Georgia, PTB, and St-Petersburg-INCART. After fine-tuning on PTB-XL, the ViT-S/JEPA model reaches 0.945 AUC on the all-statements diagnostic task, exceeding prior self-supervised ECG baselines including CPC and ST-MEM. Developed at the Zuse Institute Berlin.

Patch-based masked-training framework for robust arrhythmia detection from digitized, multi-layout ECG images (e.g. 3x4, 2x6, 12x1 printed/scanned layouts), designed to handle the asynchronous lead timing and partial signal blackout that digitization introduces. An adaptive variable block-count masking strategy focuses model attention on key patches with cross-lead dependencies. Evaluated on PTB-XL digitized into multiple synthetic layouts and externally validated on 400 real digitized ECG images from Chaoyang Hospital, outperforming classical imputation baselines and the CNN foundation model ECGFounder.

Single-lead ECG foundation model pretrained with clinically-guided contrastive learning: rather than relying on hand-labeled tasks, it uses routinely collected clinical metadata and risk scores from 161,000 MIMIC-IV-ECG patients as the training signal. Released in three sizes - Small (~448K parameters), Medium (30.7M), and Large (~296M) - and benchmarked against other ECG foundation models like ECGFounder across 18 tasks and 7 held-out datasets. Developed by Nokia Bell Labs.

Open ECG foundation model with 90.9M parameters, built on a wav2vec 2.0-style Transformer and pretrained on 1.25-1.5 million ECGs using a hybrid contrastive-and-generative self-supervised objective. Base pretrained weights and MIMIC-IV-ECG-finetuned downstream checkpoints are both released. Developed on the fairseq_signals framework by the University of Toronto / Vector Institute's Wang lab.

GPT-style decoder-only Transformer pretrained via next-token prediction on tokenized single-lead ECG time series, producing an interpretable general-purpose model that can be fine-tuned for tasks like arrhythmia screening and beat detection. Individual attention heads are shown to respond to physiologically meaningful features such as the P-wave, and token embeddings cluster by position in the cardiac cycle. A companion PPG-pretrained model (PPG-PT) is released in the same repository. Developed at Imperial College London.

Treats ECGs as a language: a QRS-Tokenizer converts raw waveforms into discrete heartbeat 'words' from a learned 8,192-entry vocabulary, and a spatio-temporal transformer (ST-ECGFormer) is pretrained via masked-sentence modeling over these tokens. Evaluated for robust, competitive performance across six public ECG datasets and published at ICLR 2025. Developed by Peking University's digital health group, pretrained on MIMIC-IV-ECG.

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.