Cardiac Arrhythmia Classification Using Wavelet Analysis of Electrocardiograms
Bibliographic record
Abstract
<p>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. </p> <p>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 <strong>77% </strong>and <strong>81% </strong>respectively, (ii) for the two group classification of AA, Paul CW and M-pattern ACW attained <strong>76% </strong>and <strong>86% </strong>respectively, (iii) for the two group classification of VA, Bump CW and M-pattern ACW attained <strong>92% </strong>and <strong>94% </strong>respectively. </p>
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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 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".