SEGMENTED MEDICAL IMAGES BASED SIMULATIONS OF CARDIAC ELECTRICAL ACTIVITY AND ELECTROCARDIOGRAM: A MODEL COMPARISON.
Bibliographic record
Abstract
Abstract. — The purposes of this work is to compare the action potential and electrocardiogram computed with the monodomain and bidomain models, using a patient-based two-dimensional geometry of the heart-torso. The pipeline from CT scans to image segmentation with an in-house level set method, then to mesh generation is detailed in the article. Our segmentation technique is based on a new iterative Chan-Vese method. The bidomain model and its approximation called the “adapted ” monodomain model are next introduced. The numerical methods used to solve these two models are briefly presented. Using both uni- and two-dimensional test cases, we next assess the mesh size required to control the error on the conduction velocities, a main source of error in cardiac action potential computations. We show with quantitative estimates that a main parameter controlling the mesh size is the cell membrane surface-to-volume ratio, noted χ. Realistic χ of about 1500 − 2000 cm−1 for human hearts still require major computational resources. We then compare our numerical solutions and the electrocardiogram recovered with both models on our heart-torso geometry. Activation sites are chosen so that the depolarisation isochrons closely match experimental results in human hearts for healthy cardiac propagation. Both models give similar solutions whereas the bidomain model is about 20-50 times more CPU intensive than the adapted monodomain model. The main computational effort goes in the computation of the extra-cellular and extra-cardiac potentials in the heart-torso. We show that the equations for these potentials must be solved with sufficient accuracy, otherwise compromising the quality of the computed electrocardiograms. Key words and phrases. — Electro-cardiology, bidomain and monodomain models, image segmentation, ECG simulation, reaction diffusion equations, numerical
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".