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 34 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> on the hidden test set.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".