Blind Source Separation in the Analysis of Electrocardiogram Pre-Shock Waveforms During Ventricular Fibrillation
Bibliographic record
Abstract
Ventricular fibrillation (VF) is a lethal cardiac arrhythmia and electric shock is the only available treatment option for it. Existing works focus on predicting shock success to help improve cardiac resuscitation outcomes. It is desirable to extract information from the electrograms that relates to the current theories on VF mechanism and associate them to the prediction of shock outcomes. To this effect this study used a unique human VF database to evaluate the independent sources (ISs) extracted from Blind Source Separation approach (BSS) and a correlation of 88% was observed between the dominant ISs extracted using a single lead ECG with the number of rotors (i.e., sources identified using multi-channel spatio-temporal phase maps) supporting the hypothesis that the ISs are associated with the rotors. In predicting the shock outcomes using features extracted from the ISs for the given database, we achieved a classification accuracy of 68%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".