Intracardiac electrogram envelope detection during atrial fibrillation using fast orthogonal search
Bibliographic record
Abstract
The performance of any intracardiac electrogram processing method is limited by the accuracy of its activation detection approach. The most common activation detection approaches in the literature aim to find the highest peak in the activation envelope disregarding the start and end points. However, the duration of the activation can be used to extract useful information such as wave collisions. In this work, we propose a novel orthogonal based approach for fast and accurate estimation of the start and end of the activations (activation envelope) in intracardiac recordings during atrial fibrillation. Wavelet decomposition of the signals was used to create a pool of basis functions for the proposed modeling method. The database included 24 recordings of approximate length of 6s obtained from atrial endocardium of 5 patients who underwent catheter ablation therapy. The start and end of activations in each electrogram was manually annotated by an expert electrophysiologist and the annotations were used as a gold standard to calculate the performance of our envelope detection method. The results show promising performance and excellent robustness to training data for our proposed method with respect to envelope estimation error.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".