A Least Squares Approach to Estimation of Far-field Voltage in Unipolar Electrograms in Atrial Fibrillation
Bibliographic record
Abstract
The evaluation of atrial scar using electrogram voltage is emerging as a promising approach to atrial fibrillation catheter ablation. However, unipolar electrograms recorded from intracardiac catheters during AF are corrupted by far-field signals from remote atrial sites and the ventricles, which results in voltage overestimation and scar underdetection. Removal of these far field signals would allow improved assessment of the local unipolar electrogram at the recording site. Most far-field removal methods consider only the ventricular far-field. In AF, the surrounding right and left atrial activity contributes to the far field and its removal has not been previously described. We present a least squares method to remove the far-field from all sources from unipolar electrograms. We tested the method on synthetic data generated from real electrograms from 80 patients undergoing AF catheter ablation. The method performed well, extracting the far-field with high accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".