Arrhythmia Classification of Reduced-Lead Electrocardiograms by Scattering-Recurrent Networks
Bibliographic record
Abstract
We describe an automatic classifier of arrythmias based on 12-lead and reduced-lead electrocardiograms. Our classifier composes the scattering transform (ST) and a long short-term memory (LSTM) network. It is trained on PhysioNet/Computing in Cardiology Challenge 2021 data. The ST captures short-term temporal ECG modulations while reducing its sampling rate to a few samples per typical heart beat. We pass the output of the ST to a depthwise-separable convolution layer which combines lead responses separately for each ST coefficient and then combines resulting values across ST coefficients. At a deeper level, 2 LSTM layers integrate local variations of the input over long time scales. We train in an end-to-end fashion as a multilabel classification problem with a normal and 25 arrhythmia classes. We used canonical correlation analysis (CCA) for transfer learning from 12-lead ST representations to reduced-lead ones. For 12-, 6-, 4-, 3- and 2-leads, team “BitScattered” Challenge metrics on the hidden validation set were 0.46, 0.44, 0.45, 0.46 and 0.43; and on the hidden test set were 0.10, 0.11, 0.10, 0.10 and 0.10, respectively, ranking 34thon the hidden test 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".