Computer Simulation of Cardiac Propagation: Effects of Fiber Rotation, Intramural Conductivity, and Optical Mapping
Bibliographic record
Abstract
Cardiac propagation characteristics such as anisotropy ratio and conduction velocities are often determined experimentally from epicardial measurements. We hypothesize that these measurements have inaccuracies due to intramural fiber rotation and transmural electrotonic interactions. We also hypothesize that optical mapping (OM) recordings compound the error, due to contributions from deeper layers. In this study, we studied propagation in a three-dimensional computer model of a slab of tissue with varying thickness and a 120° fiber rotation. Simulation results were further processed to reconstruct OM signals. As expected, simulation results demonstrated that the direction of wave propagation on the epicardial surface is not aligned with the epicardial fiber orientation. This angle difference was most pronounced for thin tissue, and decreased with decreasing intramural conductivity and increasing tissue thickness. This difference also increased with time elapsed poststimulus, as the contribution from deeper layers increased. Observations were confirmed experimentally with OM measurements from isolated rat hearts. Simulations also predicted that OM causes an additional error in measurements due to activity in deeper layers being less aligned. Several alternative approaches for the estimation of fiber orientation and anisotropy ratio were evaluated. Those based on conduction velocity measurements yielded the most accurate estimates when applied to noise-free simulated 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".