Fibrillation Complexity as a Predictor of Successful Defibrillation
Bibliographic record
Abstract
A major focus of Implantable Cardioverter Defibrillator (ICD) research has been to reduce the defibrillation shock energy to prolong battery life and provide an enhanced quality of life for the patient. We investigated whether the degree of disorganization (complexity) of the electrogram is correlated with defibrillation shock outcome. The study data sets were recorded using the high voltage leads of an ICD during device implantation. A total 57 data segments from 19 patients were analyzed. Beat cycles were identified using a novel wavelet based method. Two algorithms were proposed and implemented to quantify the disorganization of the electrogram signals: Approximate Entropy and Cross Correlation. Entropy Index based on the ApEn method, was able to discriminate successful episodes from failure ones with a specificity of 93% and sensitivity of 100%. Similarity Index based on Cross correlation method, obtained a specificity of 72% and sensitivity of 66%. We conclude that the organization of a VF episode is related to the minimum energy required for successful defibrillation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".