HuBERT-ECG as a self-supervised foundation model for broad and scalable cardiac applications
Bibliographic record
Abstract
Abstract The electrocardiogram (ECG) is a widely accessible tool for cardiovascular assessment, and the growing availability of ECG datasets has enabled the emergence of ECG foundation models. However, such foundation models often lack extensive evaluation across clinically heterogeneous downstream tasks extending beyond conventional rhythm and conduction analysis. We present HuBERT-ECG, a self-supervised foundation ECG model pre-trained on 9.1 million 12-lead ECGs from four countries and diverse patient populations, and evaluated through fine-tuning on 21 independent datasets spanning more than 1.6k diagnostic and prognostic targets across adults and paediatric cohorts, including single-lead settings. These tasks cover conditions for which the ECG is the primary diagnostic modality, provides supportive but non-definitive diagnostic information, or enables acute-care prediction and prognostic modelling. Available in three model sizes to characterise scaling behaviour and support diverse computational constraints, HuBERT-ECG achieves AUROC ranging from 84% to 99% on ECG-primary diagnostic tasks, 76% to 97% on supportive diagnostic tasks, 74% to 91% on prognostic prediction tasks, and 88% to 92% on single-lead ECG benchmarks. Moreover, a large-scale multitask fine-tuning across 2.4 million subjects and 164 tasks simultaneously shows that AUROC further increases for clinically relevant tasks without extra task-specific supervision. We release pretrained models and code as building baselines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".