Discriminative sparse coding in the analysis of electrocardiogram during ventricular arrhythmias
Bibliographic record
Abstract
Abnormalities in the rhythmic electromechanical contractions of the heart results in cardiac arrhythmias. When these abnormalities rise from the ventricles of the heart, they are classified as ventricular arrhythmias. The two major types of ventricular arrhythmias are ventricular tachycardia (VT) and ventricular fibrillation (VF). Ventricular fibrillation is the most dangerous among the two arrhythmias, that usually leads to sudden cardiac death if not treated immediately. Annually about 40,000 sudden cardiac deaths are reported in Canada. Due to high mortality rate and serious impact on quality of life, researchers have been focusing on characterizing ventricular arrhythmias that may lead to delivering optimized treatment options in improving the survival rates. In this thesis two major types of ventricular arrhythmias were analyzed and quantified by performing discriminative sparse coding analysis called label consistent K-SVD using time frequency dictionaries that are well localized in time and frequency domains. The analyzed signals were 670 ECG ventricular arrhythmia segments from 33 patients extracted from the Malignant Ventricular Ectopy and Creighton University Tachy-Arrhythmia databases. Using the LCKSVD dictionary learning approach, an overall maximum classification accuracy of 73.3% was achieved with a hybrid optimized wavelet dictionary. Based on the comparative analysis, the trained (learned) dictionaries yielded better performance than the untrained dictionaries. The results indicate that discriminative sparse coding approach has greater potential in extracting signal adaptive and morphologically discriminative time-frequency structures in studying ventricular arrhythmias.
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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 | 0.000 | 0.001 |
| 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 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".