Atrial fibrillation classification using step-by-step machine learning
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper presents a detailed overview of our submission to the 2017 Physionet Challenge where competitors were asked to build a model to classify a single lead ECG waveform as either normal sinus rhythm, atrial fibrillation, other rhythm, or noisy. A step-by-step machine learning pipeline was assembled, which included signal conditioning, R-peak detection and filtering, and feature extraction. A suite of over 300 features, falling into one of three main feature groups; template features, RRI features, and full waveform features, were extracted from each waveform and an XGBoost, tree-based, gradient boosting classifier was used as the machine learning algorithm. The model produced a cross-validation F 1 score of 0.8245, a hidden sub-test score of 0.82, and a hidden test score of 0.8125. The score breakdown for each class (normal sinus rhythm, atrial fibrillation, other rhythm, and noisy) was as follows: F 1, NRS = 0.9024, F 1, AF = 0.8156, F 1, OR = 0.7194, F 1, Noise = 0.5705.
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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 it