Automatic Analysis of Activation Recovery Interval in Heterogeneous Fibrotic Tissue from Chronically Infarcted Swine
Bibliographic record
Abstract
Scar-related myocardial tissue can lead to ventricular arrhythmia (VA), a global concern for sudden cardiac death.Repolarization dispersion due to electrical remodeling within infarcted territory often triggers ventricular arrhythmias.However, evaluating ventricular repolarization globally is clinically challenging and there is no gold-standard approach.This paper introduces a new method using body-surface leads to automate activation (ATs) and recovery times (RTs) detection in unipolar electrograms (UEs).Multilevel Discrete Wavelet Transform sets activation and recovery detection windows based on external lead data.Then, Wyatt's method is used to compute ATs and RTs.By analyzing catheter-based intracardiac electrograms from n=9 infarcted swine, we compare ARI values between healthy tissue and arrhythmia-prone border zones (BZ), and between normal sinus rhythm (NSR) and pacing.Results reveal significant ARI differences between NSR and pacing, and among tissue types, with heterogeneous ARI values highlighting the repolarization complexity within BZ.This emphasizes the necessity for new automated approaches in assessing and treating cardiac arrhythmias, acknowledging the diverse electrophysiological individual profiles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".