Novel Automated Paced Fractionation Detection Algorithm for Ablating Ventricular Tachycardia
Bibliographic record
Abstract
Catheter ablation therapy has become a key intervention in treatment of ventriculartachycardia (VT). However, current fractionation mapping methods used to isolate the ablation targets in VT patients are done manually, and are therefore time consuming. They also have limited success rates (50% recurrence rate within 2 years). We present a fully automated fractionation detection algorithm for patients with VT which expands on previously defined fractionation features and which substantially decreases associated study times. Paced electrogram signals were collected from six patients during electrophysiologic study according to a modified paced electrogram fractionation analysis protocol. Data were exported and analyzed offline using custom written software. Electrograms from right ventricular pacing catheter were used as reference. Surface electrograms, along with ventricular geometry and relative catheter locations, were used to identify physiological interference and physiologically irrelevant features. A total of 264 electrograms, collected from a roving catheter, were manually and automatically annotated for fractionation as defined by three features: conduction time (CT), electrogram duration (ED), and number of deflections (ND). Of these, 60 were selected manually to have no discernable features and were successfully discarded by our algorithm; yielding a specificity of 100%. Of the remaining 204, 16 were erroneously discarded by our algorithm; yielding a sensitivity of 92.16%. A comparison between annotations showed correlations of 0.98, 0.97, and 0.94 for AL, ED, and ND respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".