Characterization of fractionated electrograms using a novel time-frequency based algorithm
Bibliographic record
Abstract
Atrial fibrillation (AF) arises from complex spatiotemporal atrial activation. Current treatment for patients with AF when antiarrhythmic drugs have failed is catheter ablation which uses Radiofrequency (RF) energy to destroy heart tissues that drive AF. Therefore, AF can be terminated once the AF source is localized and eliminated by RF ablation. There is considerable interest in defining whether complex fractionated atrial electrograms (CFAE) indicate AF-perpetuation sites. This work proposes a novel time-frequency (TF) based algorithm to characterize CFAE electrograms (EGMs). The proposed technique obtains an automated classifier that is trained based on the differences evidenced between the TF structures of CFAE and non-CFAE EGMs. These characteristics are quantified using 5 TF features which are extracted using a TF matrix decomposition method performed on the EGM. The results from 5 patients with AF show that the proposed method is successful in identifying CFAE vs. non-CFAE EGMs, and might open new perspectives for a novel and reliable mapping technique to accurately characterize and understand AF mechanism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".