Two computationally efficient intracardiac conduction velocity estimators: Error analysis for the planar wavefronts
Bibliographic record
Abstract
The cardiac conduction velocity vector (VV) provides insight into the characteristics of the myocardial tissue and can be deployed to treat different heart diseases. The cardiac VV can be estimated by processing the intracardiac electrograms. In this paper, we analyse the VV estimation error of two computationally efficient heuristic estimators: 1-maximum time difference (MTD) and 2-minimum velocity (MinV). In the MTD, the estimated velocity is a vector in the direction of the vector that connects the first to the last catheter's activated electrode, and the magnitude of the VV is estimated as the ratio of the distance between two electrodes to the delay between their ATs. In the MinV, using a similar procedure as the MTD, we assign a VV to each available electrode pair of the catheter, and the VV with the smallest magnitude is selected as the final estimated VV. We derive the average absolute value and average squared absolute value of the VV estimation error of the MTD and MinV methods for a planar wavefront in a two-dimensional case and confirm the accuracy of our analysis by simulating synthetic data.
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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 | 0.005 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".