Tracking Rotors With Minimal Electrodes
Bibliographic record
Abstract
BACKGROUND: High-frequency periodic sources during cardiac fibrillation can be detected by phase mapping techniques. To enable practical therapeutic options for modulating periodic sources (existing techniques require high density multielectrode arrays and real time simultaneous mapping capability), a method to identify electrogram morphologies colocalizing to rotors that can be implemented on few electrograms needs to be devised. METHOD AND RESULTS: Multichannel ventricular fibrillation electrogram data from 7 isolated human hearts using Langendorff setup and intraoperative clinical data from 2 human hearts were included in the analysis. The spatial locations of rotors were identified using phase maps constructed from 112 electrograms. Electrograms were analyzed for repeating patterns and discriminating signal morphologies around the locations of rotors and nonrotors were identified and quantified. Features were extracted from the unipolar electrogram patterns, which corroborated well with the spatial location of rotors. The results suggest that using the proposed modulation index feature, and as low as 1 sample point in the vicinity of the rotors, an accuracy as high as 86% (P<0.001) was obtained in separating rotor locations versus nonrotor locations. The analysis of bipolar electrogram signatures in the vicinity of the rotor locations suggest that 62.5% of the rotors occur at locations where the bipolar electrogram demonstrates continuous activities during ventricular fibrillation. CONCLUSIONS: Unipolar electrogram extracted modulation index-based detection of rotors is feasible with few electrodes and has greater detection rate than bipolar approach. This strategy may be suitable for nonarray-based single mapping catheter enabled detection of rotors.
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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 | 0.001 |
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".