Generative Data by β-Variational Autoencoders Help Build Stronger Classifiers: ECG Use Case
Bibliographic record
Abstract
We explore the challenge of learning models that use electrocardiogram (ECG) data to diagnose various cardiovascular diseases. Here, we explore whether classifiers trained on a dataset of real labeled ECGs, augmented with synthetic ECGs, can perform better than ones trained on unaugmented datasets. We first used a dataset of ECGs, each labelled with one or more of 15 diagnoses, from 244,077 patients to train an unsupervised $\beta$-VAE model, that could generate time series of 12-lead ECG signals for each of the diagnoses. We then used this generative model to generate ECGs with the ST-segment Elevated (STE) abnormality, which we added to the public dataset of ECG abnormalities (n = 6877, over normal (Sinus Rhythm) and 8 different abnormalities) of China Physiological Signal Challenge 2018, and found a learner trained on this extended dataset performed better than one trained on only the original data on the targeted STE label but also enhanced its performance for the classification of 4 other labels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".