Toward Robust Automated Cardiovascular Arrhythmia Detection using Self-supervised Learning and 1-Dimensional Vision Transformers
Bibliographic record
Abstract
Cardiovascular diseases are the primary cause of death globally.With the prevalence of electrocardiogram machines both within and outside the clinical environment, it is now possible to passively monitor a patient's heartbeat for cardiovascular diseases long before they become a cause of concern.However, the most significant problem currently prohibiting the wide-scale deployment of automated electrocardiogram systems is the potential for false alarms, leading to a condition known as "alarm fatigue".Of the major culprits causing such issues, noise in electrocardiogram data can often masquerade as instances of acute cardiovascular diseases.Moreover, incorrect labels provided by domain experts can bias models to repeat the same mistakes learned during training.Recently, as substantial amounts of unlabelled electrocardiogram data have become publicly available, self-supervision has emerged as an increasingly viable part of the pre-training process.This work begins by examining the importance of self-supervised learning for arrhythmia detection, demonstrating significant performance improvements as it reduces overfitting to class imbalance and noise.A new method for self-supervised pre-training on electrocardiogram data is proposed, obtaining SOTA results while simultaneously reducing the pre-training time by one-fifth and increasing the model's capacity by a factor of 14, providing a new foundational model for future research.Finally, this work investigates multiple solutions for addressing the significant noise and class imbalance concerns in the electrocardiogram data and label set.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".