Mapping of Cardiac Electrophysiology Onto a Dynamic Patient-Specific Heart Model
Bibliographic record
Abstract
Electrophysiological cardiac data mapping is an essential tool for the study of cardiac rhythm disorders, such as atrial fibrillation. Over the past decade, various advanced cardiac mapping systems have been developed to create detailed cardiac maps and assist physicians in diagnosis and therapy guidance. While these systems have increased the ability to study and treat cardiac arrhythmias, inherent limitations exist. The objective of this paper is to describe and evaluate a system that extends current approaches to cardiac mapping, to create a dynamic cardiac map, using patient-specific cardiac models. This paper details novel approaches to collecting a stream of electrophysiological cardiac data, registering the data with patient-specific dynamic cardiac models, and displaying the data directly on the dynamic model surface, giving a more accurate and comprehensive visualization environment when compared to current systems. To validate the system, a series of laboratory and in vivo experiments were conducted. In the laboratory studies, the system was used to test the user's ability to accurately locate a landmark in physical space, as well as their ability to accurately navigate to a virtual location. In the in vivo studies the overall system performance was compared to an existing electrophysiological recording system, where right atrial cardiac maps were created during sinus and paced cardiac rhythms. The results showed that the new dynamic cardiac mapping system was able to maintain high accuracy in locating physical and virtual landmarks, while being able to create a dynamic cardiac map displayed on a dynamic cardiac surface model.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".