Focal and pseudo/rotational activations in human atrial fibrillation defined with automated periodicity mapping
Bibliographic record
Abstract
INTRODUCTION: Defining atrial fibrillation (AF) wave propagation is challenging unless local signal features are discrete or periodic. Periodic focal or rotational activity may identify AF drivers. Our objective was to characterize AF propagation at sites with periodic activation to evaluate the prevalence and relationship between focal and rotational activation. METHODS: We included 80 patients (61 ± 10 years, persistent AF 49%) from the FaST randomized trial that compared the efficacy of adjunctive focal site ablation versus pulmonary vein isolation. Patients underwent left atrial (LA) activation mapping with a 20-pole circular catheter during spontaneous or induced AF. Five-second bipolar and unipolar electrograms in AF were analyzed. Periodic sites were identified by spectral analysis of the bipolar electrogram. Activation maps of periodic sites were constructed using an automated, validated tracking algorithm, and classified into three patterns: focal sites (FS), rotation (RO), or pseudo-rotation (pRO). RESULTS: The most common propagation pattern at periodic sites was FS for 5-s in all patients (4.9 ± 1.9 per patient). RO and pRO were observed in two and seven patients, respectively, but were all transient (3-5 cycles). Activation from a FS evolved into transient RO/pRO in five patients. No patient had autonomous RO/pRO activations. Patients with RO/pRO had greater LA surface area with periodicity (78 ± 7 vs. 63 ± 16%, p = .0002) and shorter LA periodicity CL (166 ± 10 vs. 190±28 ms, p = .0001) than the rest. CONCLUSION: Using automated, regional AF periodicity mapping, FS is more prevalent and temporally stable than RO/pRO. Most RO/pRO evolve from neighboring FS. These findings and their implications for AF maintenance require verification with global, panoramic mapping.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".