Activation detection of intracardiac electrogram during atrial fibrillation based on the variance equality test
Bibliographic record
Abstract
Performance of the algorithms which process intracardiac electrograms (IEGMs) highly depends on the accuracy of estimating the times that electrical waves pass the area under the electrodes. Estimating these activation times (ATs) from IEGMs during atrial fibrillation (AF) is extremely challenging as electrical activities of atria are very complex, non-stationary, and irregular. In this paper, we propose a new activation detector which is based on the test of the equality of variance of two sets of data. At any time t, we consider two sets of IEGM data: 1) data in a bounded interval around t, 2) data in bounded intervals around the first interval. We show that the activation zone can be extracted by comparing the variance of these two sets, i.e., we introduce a new preprocessing approach and show that it can effectively highlight activation zones of IEGMs. Our simulation results on bipolar atrial IEGMs gathered during AF confirm the efficiency of the proposed preprocessing method.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".