CardioDiverse: A Balanced ECG Dataset for Improved Association of Heart Diseases with Relevant Leads
Bibliographic record
Abstract
This study introduces CardioDiverse, a new dataset that has been constructed to address the scarcity of balanced data in existing electrocardiogram (ECG) datasets. CardioDiverse is a combination of multiple datasets integrated to provide rich information to support the association between the different ECG leads and CVD subclasses. The dataset is available to the public through GitHub. Our findings illustrate that particular leads provide clear manifestation to certain CVD subclasses. For instance, leads II, AVF, V3, V4, V5, and V6 were predominantly associated with ST-T wave abnormality, whereas leads II, III, AVF, V1, V5, and V6 were more affiliated with hypertrophy. Furthermore, by extending the analysis to include specific subclasses within the PTB-XL dataset, our study shows promising results in categorizing these subclasses using single-lead ECGs. CardioDiverse is an invaluable resource for researchers, as it provides a more balanced dataset and includes essential data analysis tools within the repository. The data and insights obtained through this study pave the way for future research to investigate the use of these leads for the early detection and diagnosis of cardiovascular diseases.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".