An algorithm for rotor tracking in atrial fibrillation using graph search-based periodic peak detection
Bibliographic record
Abstract
Rotors are rotating electrical waves that may sustain atrial fibrillation (AF); thereby providing therapeutic targets for catheter ablation. We propose a method for identifying rotors from circular catheter recordings of bipolar intracardiac electrograms (EGM) during AF. We use dominant frequency-based periodicity detection along with a graph search algorithm to identify the most dominant periodic activations or peaks of interest in each bipolar EGM recorded by a multipolar circular catheter. We then track the activations across catheter bipoles to determine whether they conform to the rotational pattern of a rotor. The performance of the proposed method is tested on simulated bipolar EGM arrays containing rotor activation corrupted by noise and complex aperiodic signal features. The method is shown to perform with high accuracy (up to 98% sensitivity and 100% specificity) in detecting simulated rotors and may serve to guide rotor ablation in patients with AF.
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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 | 0.000 | 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".