Ventricular fibrillation detection algorithm for automated external defibrillators
Bibliographic record
Abstract
The automatic external defibrillator (AED) is a lifesaving device, which processes and analyzes the electrocardiogram (ECG) and prompts a defibrillation shock if ventricular fibrillation is determined. This project investigates the possibility of developing a Ventricular Fibrillation (VF) detection algorithm based on Autoregressive Modeling (AR Modeling) and dominant poles for the use in AEDs. In particular, the ECG segment is modeled using AR modeling and the dominant poles are extracted from the model transfer function. The dominant pole frequencies were then used in classification based on the distance measure. The potential use of this method to distinguish between VF and Normal sinus rhythm (NSR) is discussed. The method was tested with ECG records from the widely recognized databases of American Heart Association (AHA) and the Creighton University (CU). Sensitivity and specificity for the new VF detection method were calculated to be 66% and 94% respectively. The proposed method has some advantages over other existing VF detection algorithms; it has a high detection accuracy, it is computationally inexpensive and can be easily implemented in hardware.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".