Modified maximum time difference intracardiac conduction velocity estimation
Bibliographic record
Abstract
The cardiac conduction velocity vector (VV) at a desired point in any chamber of the heart can be estimated by processing the local intracardiac electrograms, i.e., the activation times (ATs) and the locations of the recording catheter's electrodes can be used for the VV estimation. In this paper, we modify the maximum time difference (MTD) method, which is a simple computational efficient cardiac VV estimation approach. In the MTD, first, the ATs of the electrograms are extracted, and the corresponding wavefronts are estimated. For each wavefront, the VV is estimated as the vector connecting the first to the last activated electrodes of the catheter divided by the time duration between the activation of those two electrodes. In the proposed modified MTD (MMTD) approach, which is slightly more computationally complex than the MTD, we divide the ATs of each considered wavefront into two groups, such that one group contains the electrodes with early activations, and the other contains those with late activations. We properly assign a time value and a location value to these groups and follow the same procedure as the MTD method to estimate the VV using the assigned values. Using synthetic data, we show that the proposed MMTD improves the quality of the cardiac conduction VV estimation of the MTD method, i.e., the MMTD is more robust to the AT estimation error and is able to determine the VV of the planar wavefronts more accurately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".