Analysis Of Electrocardiograms During Human Ventricular Arrhythmias For Optimizing Treatment Options
Bibliographic record
Abstract
Cardiovascular diseases are diseases that arise from abnormal medical conditions of the heart and the circulation system. Ventricular arrhythmias are a subset that originates from rhythm disorders of the lower chambers (ventricles) of the heart. In spite of research and technology advancements, annually 350,000 sudden cardiac deaths are reported in North America (45,000 in Canada) most of which are ventricular fibrillation (VF) related. This serves as a strong motivation to improve upon or optimize the choice of current treatment options from an engineering perspective which could eventually help reduce the number of SCDs. The choice of the treatment vary in general based on the following two categories of affected population and the type of arrhythmia: (1) symptomatic patients who are prone to or have had arrhythmia occurrences and are currently under medical care and (2) people who suffer ventricular arrhythmias in an out-of-the-hospital environment. This thesis, by employing advanced signal analysis, attempts to improve the characterization of the ventricular arrhythmias, thereby providing better iscriminatory clues in assisting clinicians and emergency medical staff (EMS) to arrive at optimal treatments options for both the categories of affected population. In the study of symptomatic patients, the organizational structure of the arrhythmia was quantified using wavelet-singular value decomposition analysis, which lead to a novel sub-classification of the ventricular arrhythmia. Classification accuracies of 93.7% for ventricular tachycardia (VT)/non-VT classification and 80% for organized-VF /disorganized-VF classification were achieved. In the study of out-of-the-hospital arrhythmia instances, focal structural variations were analyzed using wavelets, which led to identifying a signal pattern that could serve as an important clue for the EMS personnel to improve the resuscitation outcomes. Using a database of 25 out-of-the hospital arrhythmia segments, the proposed analysis yielded a classification accuracy of 80%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".