Non-invasive epicardial imaging of human ventricular fibrillation
Bibliographic record
Abstract
The spatial distribution of ECG torso potentials during ventricular fibrillation (VF) might provide useful information about underlying electrical dynamics. We used an inverse solution technique to non-invasively construct images of epicardial activity of human VF. A 120-lead mapping system was used to record body surface potential maps (BSPM) from eight anesthetized patients during VF induction following implantable defibrillator placement. Epicardial potential maps of VF were derived mathematically by inverse solution using Tikhonov regularization and L-curve method, assuming a homogenous bounded torso. To assess accuracy, VF was simulated in a large-scale numerical anisotropic heart model incorporating ionic currents. Potential fields were simulated within the torso volume conductor and on the body surface by forward solution to assess the degree of spatial information attenuation. Calculated inverse solution was compared with epicardial activity on the heart model. Spatial features of VF attenuate with distance from the heart due to the volume conductor; however, the model results demonstrate that inverse solution can resolve epicardial VF patterns to a limited, but potentially useful, degree with larger spatial scales being preserved.
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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 | 0.000 |
| 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 | 0.000 | 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".