A depth-averaged heart model for the inverse problem of cardiac electrophysiology
Bibliographic record
Abstract
Abstract The inverse problem of cardiac electrophysiology is notoriously ill-posed, but is nonetheless extremely useful. In particular, it is difficult to reconstruct the transmembrane voltage in the volume of the heart, since an infinite dimensional space of transmembrane voltages can produce the same observation on the body surface. A widely used alternative is to consider only the outer surface of the heart, the epicardium, and solve a Cauchy problem for the Laplace equation in the torso domain. However, this approach only allows reconstruction of the extracellular potential on the epicardium, thus missing the information contained in the volume of the heart and in the transmembrane voltage. We propose a new methodology for reconstructing activation maps from torso surface data, which incorporates information from the myocardial volume, while solving a surface problem on the heart. We formulate a static forward model, derived from the bidomain model, by averaging equations in the heart. The averaged equations are coupled with the usual Laplace equations in the surrounding domains. For solving the inverse problem, this «depth-averaged» forward model is used as a constraint in an optimal control problem that allows to recover depth-averaged transmembrane voltage and extracellular potential in the heart, corresponding to observations on the body surface. Activation maps are computed by post-processing the recovered signals in the heart. This method retains the ability to include the interactions between the heart, torso and blood cavities, while maintaining the simplicity of the usual inverse procedure. The inverse problem is solved with a simple linear system, using the Lagrangian formalism, and thus in a single iteration. We emphasize on the post-processing techniques for recovering activation maps. We observe that using a threshold on the transmembrane voltage allows to recover smoother and more accurate activation maps than with a maximal deflection method on the extracellular potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".