Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options
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Bibliographic record
Abstract
Ventricular arrhythmias arise from abnormal electrical activity of the lower chambers (ventricles) of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two major subclasses of ventricular arrhythmias. While VT has treatment options that can be performed in catheterization labs, VF is a lethal cardiac arrhythmia, often when detected the patient receives an implantable defibrillator which restores the normal heart rhythm by the application of electric shocks whenever VF is detected. The classification of these two subclasses are important in making a decision on the therapy performed. As in the case of all real world process the boundary between VT and VF is ill defined which might lead to many of the patients experiencing arrhythmias in the overlap zone (that might be predominately VT) to receive shocks by the an implantable defibrillator. There may also be a small population of patients who could be treated with anti-arrhythmic drugs or catheterization procedure if they can be diagnosed to suffer from predominately VT after objectively analyzing their intracardiac electrogram data obtained from implantable defibrillator. The proposed work attempts to arrive at a quantifiable way to scale the ventricular arrhythmias into VT, VF, and the overlap zone arrhythmias as VT-VF candidates using features extracted from the wavelet analysis of surface electrograms. This might eventually lead to an objective way of analyzing arrhythmias in the overlap zone and computing their degree of affinity towards VT or VF. A database of 24 human ventricular arrhythmia tracings obtained from the MIT-BIH arrhythmia database was analyzed and wavelet-based features that demonstrated discrimination between the VT, VF, and VT-VF groups were extracted. An overall accuracy of 75% in classifying the ventricular arrhythmias into 3 groups was achieved.
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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 it