University of Alberta CARDIAC VIDEO ANALYSIS USING HODGE HELMHOLTZ FIELD DECOMPOSITION By
Bibliographic record
Abstract
Ventricular fibrillation (VF) is an extremely rapid, highly irregular heart arrhythmia originating in the ventricles. When the VF occurs, the heart loses its capability of pumping blood, and the patients die within minutes unless the VF is immediately stopped. The mechanisms of the VF are still not completely understood. Several hypotheses suggest that it is important to extract the pure expanding component and the pure rotational component from the cardiac electrical patterns. In this thesis, we first implement the 2-D discrete Hodge-Helmholtz field decomposition (DHHFD) based on regular triangular grids such that it can be directly used for video analysis. We then analyze the optical flow of the cardiac electrical patterns using the 2-D DHHFD. The pure expanding and the pure rotational motion components of the cardiac electrical signals are extracted. Analyses of the decomposed motion components have shown that the VF might be caused by the strong rotational components of the dynamical cardiac electrical patterns. Techniques have also been developed to detect the dominant critical points such as sources, sinks, and rotational centers in the cardiac electrical patterns. The critical points provide important clues for describing and understanding the abnormal propagation of the cardiac electrical signals.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".