Eikonal-based initiation of fibrillatory activity in thin-walled cardiac propagation models
Bibliographic record
Abstract
Reentrant arrhythmias can be simulated in electrophysiological models of electrical impulse propagation governed by a reaction-diffusion system. To facilitate the initiation of a large number of independent episodes of simulated arrhythmias with controllable level of complexity, a new approach is proposed for thin-walled geometries in which depolarization wave dynamics is essentially two-dimensional. Points representing phase singularities are first randomly distributed over the epicardial surface and are assigned a topological charge (direction of rotation). A qualitatively-correct phase map is then reconstructed on the whole surface by interpolation. The eikonal-diffusion equation is used to iteratively regularize the phase map based on a priori information on wavefront propagation. An initial condition for the reaction-diffusion model is created from the resulting phase map with multiple functional/anatomical reentries. Results in an atrial model demonstrate the ability to generate statistical realizations of the same dynamics and to vary the level of complexity measured by the number of phase singularities. A library of 100 simulations with an average number of phase singularities ranging from 1 to 10 is created. An extension to volumetric patient-specific atrial models including fiber orientation and a fast conducting system is presented to illustrate possible applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".