Sequential Intracardiac Activation Time Mapping of Arrhythmias Without Fiducial Time References
Bibliographic record
Abstract
Sequential local activation time (LAT) mapping of intracardiac electrograms' activations requires a stable reference signal to align recording phases. OBJECTIVE: This work's purpose is to develop an LAT mapping approach that does not rely on a time-alignment reference (TAR). METHODS: To create an LAT map in absence of TAR (TARLess), the coordinates and LATs of recording electrodes are collected sequentially; a bank of candidate functions (CFs) is constructed that contains constant binary level CFs and non-linear functions of recording points' coordinates. Finally, a subset of CFs is linearly combined to create an activation time function with output matching electrodes' LATs. Synthetic and clinical data were deployed to validate TARLess. A simple two-dimensional computer model was used to create 30 different wavefront collision scenarios in a region with spatial conduction heterogeneities. Furthermore, sequential recordings were collected from seven atrial fibrillation patients during stimulation from one or two sites, after sinus rhythm was achieved post catheter ablation. RESULTS: We showed that TARLess maps are similar to the one that uses TAR; for the 20 clinical maps, the mean absolute difference between measured LAT with the TAR and TARLess approach was 5.2 ±2.0 milliseconds. CONCLUSION: We developed a novel method to create an LAT map of sequential recordings without using any TAR and showed that it can create accurate maps even during the collision of multiple wavefronts. SIGNIFICANCE: TARLess mapping does not require a reference catheter which could lead to reduction in ablation procedure duration, cost, and potential complications.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".