Resolving Myocardial Activation With Novel Omnipolar Electrograms
Bibliographic record
Abstract
BACKGROUND: With its inherent limitations, determining local activation times has been the basis of cardiac mapping for over a century. Here, we introduce omnipolar electrograms that originate from the natural direction of a travelling wave and from which instantaneous conduction velocity amplitude and direction can be computed at any single location without first determining activation times. We sought to validate omnipole-derived conduction velocities and explore potential application for localization of sources of arrhythmias. METHODS AND RESULTS: Electrograms from omnipolar mapping were derived and validated using 4 separate models and 2 independent signal acquisition methodologies. We used both electric signals and optical signals collected from monolayer cell preparations, 3-dimensional constructs built with cardiomyocytes derived from human embryonic stem cells, simultaneous optical and electric mapping of rabbit hearts, and in vivo pig electrophysiology studies. Conduction velocities calculated from omnipolar electrograms were compared with wavefront propagation from optical and electric-mapping studies with a traditional local activation time-based method. Bland-Altman analysis revealed that omnipolar measurements on optical data were in agreement with local activation time methods for wavefront direction and velocity within 25 cm/s and 30°, respectively. Similar agreement was also found on electric data. Furthermore, mathematical operations, such as curl and divergence, were applied to omnipole-derived velocity vector fields to locate rotational and focal sources, respectively. CONCLUSIONS: Electrode orientation-independent cardiac wavefront trajectory and speed at a single location for each cardiac activation can be determined accurately with omnipolar electrograms. Omnipole-derived vector fields, when combined with mathematical transforms may aid in real-time detection of cardiac activation sources.
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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".