Control of Cardiac Alternans in a Realistic Electromechanical Model of Cardiac Tissue
Bibliographic record
Abstract
Electrical alternans is a physiological phenomenon manifested as beat-to-beat oscillation (electric wave width alternation) of the cardiac action potential duration. Alternans has been shown to be a precursor to arrhythmias and sudden cardiac death, which is the most common cause of death in the industrialized world. The control of alternans has been explored in many studies in the literature. However, the majority of the existing control algorithms succeeded in suppressing alternans only in small pieces of cardiac muscle. To our knowledge, all the control algorithms are electric-based realization and have not considered mechanical properties of cardiac tissue, despite the fact that mechanical deformation is shown to influence the electrical activity of the heart tissue, and consequently the cardiac alternans. In a previous work, we presented a novel mechanical perturbation algorithm to control alternans. The proposed control algorithm succeeds to suppress the alternans in relevantly sized cardiac tissues. However, only a simplified mathematical model has been used for the numerical simulation and a more realistic model should be used to investigate the control of alternans which is the goal of this study. In this work, we will explore the feasibility of suppressing cardiac alternans in a realistic model by using the mechanical perturbation strategy. The electrical activity is represented by the Luo-Rudy model and the mechanical activity is represented by an active contractile tension model and the Mooney-Rivlin passive elasticity model. Numerical simulations are used to illustrate the feasibility and the effects of the proposed algorithms in suppressing cardiac alternans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".