Atrial electrogram complex fractionated entropy study
Bibliographic record
Abstract
Atrial electrogram (AEGM) signal processing started to play an important role during radiofrequency ablation (RFA) of atrial fibrillation (AF). This work uses Sample Entropy (SampEn) as a tool for quantification and evaluation of AEGM complexity and discrimination between complex fractionated AEGM (CFAE) and non CFAE. Additionally, SampEn classification capabilities and its robustness are tested against the presence of spikes and sample loss. Both signal disturbances were superimposed to the original AEGM signals. AEGM signals were measured during RFA of AF in patients recommended for RFA of AF. The database consisted of 113 AEGM signals of short length, 1.5 s, classified by experts into nonCFAE and CFAE classes. The results demonstrated that SampEn conserved its clinical validity up to a 10% of spike contamination (Cxy > 0.8) even though it was able to differentiate between CFAE and nonCFAE classes up to a spike probability of 0.5. It also proved to differentiate between nonCFAE and CFAE classes with a data loss percentage of up to 50%. In conclusion, SampEn is a low computational cost, easy to implement, and robust tool to characterize and quantify complexity of AEGM in real time.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".