A Study On Ventricular Fibrillation And Ventricular Tachyarrythmia Classification Methods Using Continuous Wavelet Transform
Bibliographic record
Abstract
Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are fatal cardiac diseases associated with cardiac arrest. It is difficult to manually classify VT and VF signals. However, precise classification of VT and VF signals can assist cardiologists to identify and ultimately prevent onset of VF or VT. In this thesis, some of the underlying features which characterize VF and VT are extracted and are used to efficiently classifying these signals. The features are acquired from energy coefficients matrices using Continuous Wavelet Transform (CWT) through application of Principal Component Analysis (PCA). The features are the vector containing newly generated energy projection coefficients and the vector containing the number of the top 99% principal components (Eigen-Values) for each case. Feature vectors are then passed through Fast Forward Neural Network (FFNN) and Leave One Out Method (LOOM) classifiers for discrimination. The results are then compared for the highest classification results for VF and VT signals.
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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 | 0.000 |
| 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 | 0.001 |
| 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".