Cardiac Arrhythmia Classification Using Wavelet Analysis of Electrocardiograms
Bibliographic record
Abstract
Cardiac Arrhythmias are heart rhythm abnormalities that seriously impact the quality of life and can be lethal. Four major types of cardiac arrhythmia that originate from atria and ventricles are atrial flutter (AFL), atrial fibrillation (AF), ventricular tachycardia (VT), and ventricular fibrillation (VF). Annually about 50,000 embolic strokes mostly due to AF and 35,000 sudden cardiac deaths mostly due to VF are reported in Canada. Accurate detection and segregation of these arrhythmia swiftly is an essential requirement for appropriate treatment. Automating this process is especially critical and valuable for implantable devices and long-term monitoring scenarios. In this thesis, with the above motivation, we analyzed the electrocardiograms (ECGs) recorded during these 4 types of arrhythmia using wavelet transform. A total of 100 ECG segments con- taining 25 ECG segments each for AF, AFL, VT and VF, obtained from well-known open source databases were used in this study. Discriminative features were extracted from the wavelet coefficients and fed to a linear discriminant analysis based classifier. Based on the proposed scheme, best classification accuracies using library wavelets and adaptive continuous wavelets (ACW) are as follows: (i) for four group classification, Paul CW and A-pattern ACW attained 77% and 81% respectively, (ii) for the two group classification of AA, Paul CW and M-pattern ACW attained 76% and 86% respectively, (iii) for the two group classification of VA, Bump CW and M-pattern ACW attained 92% and 94% respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".