Premature Ventricular Contraction Detection Based on Chebyshev Polynomials and K Nearest Neighbours Classifier
Bibliographic record
Abstract
Premature ventricular contraction (PVC) is among the most prevalent forms of arrhythmia diagnosed in clinical settings.Arrhythmias can be recognised by analysing the ECG signal.However, it takes a lot of time for cardiologists to analyse these long-term ECG signals.The fast and accurate identification of PVCs is crucial in the treatment of cardiac diseases Here; we propose a simple and promising method for detecting PVCs in long-term ECG signals.The method is based on Chebyshev polynomial coefficients and the k-nearest neighbour (KNN) classifier.The proposed approach has been experienced on the MIT-BIH Arrhythmia Database and the results of the experiments indicate high levels of accuracy, sensitivity, and specificity, with a 99.35% accuracy rate, 99.86% sensitivity rate, and 85.11% specificity rate.The results are highly pleasing, taking into account the straightforwardness of the classification system.It is possible that the suggested approach to classification could serve as an effective means of diagnosing arrhythmias.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".