Abstract 241: Pre- and Postshock Electrogram Features in Human VF and the Occurrence of Refibrillation
Bibliographic record
Abstract
Background: During cardiac resuscitation, recurrence of VF (or refibrillation) is a common problem that could affect the survival rates. Predicting the occurrence of refibrillation by analyzing the pre-shock/post-shock waveform would enable the EMS personnel to administer anti-arrhythmic drugs that could prevent refibrillation and could help in optimizing resuscitation outcomes. Objective: To evaluate wavelet based features extracted from pre-shock waveform and QT interval measurements from post-shock waveform in predicting refibrillation. Method: The human VF database used in this study consisted of 14 successful, 7 refibrillation, and 13 unsuccessful out-of-the-hospital VF tracings extracted from the Zoll defibrillators recorded by the Toronto area EMS personnel. For the wavelet analysis (continuous wavelet transform) we used the 10s pre-shock waveforms from all the 34 cases that were not corrupted by the CPR artifacts. Wavelet features that indirectly measure signal (i.e., electrogram) complexity were extracted as features. A two stage classification was performed, in the first stage the 34 cases were classified into two classes i.e., CLASS I: 14 succ. + 7 refib. cases and CLASS II: 13 unsuccessful cases, and in the second stage the correctly classified CLASS I was further classified into succ. and refib. categories. The QT interval analysis used the first 3 beats of the post-shock waveforms of the 7 refib. cases and verified if a decrease in QT interval was observed. Results: For the first stage CLASS I and CLASS II categories, we obtained a classification accuracy of 76.5 % and for the second stage succ. and refib. categories, we obtained a classification accuracy of 75%. However considering only the prediction of refib., we could correctly classify 4 out of the 7 refib. cases. An automated pattern classifier was used for the pattern classification. The QT interval analysis did not yield conclusive results for this study. Conclusions: The pre-shock electrogram features demonstrate potential in predicting refibrillation while the post-shock electrogram features were inconclusive.
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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".