Key result
The proposed method based on Chebyshev polynomial coefficients and a KNN classifier achieved 99.35% accuracy, 99.86% sensitivity, and 85.11% specificity in detecting premature ventricular contractions.
Why the study?
Manual analysis of long-term ECG signals is time-consuming, while fast and accurate identification of premature ventricular contractions is crucial in treating cardiac diseases.
A novel ECG classification method using Chebyshev polynomials and a KNN classifier achieved high accuracy and sensitivity for detecting premature ventricular contractions, offering a computationally simple approach for automated arrhythmia diagnosis.
Proposed Chebyshev PVC detection may speed ECG analysis; leaves open prospective validation before clinical use.
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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Guendouzi et al. (2023) studied Premature Ventricular Contraction (PVC) (n=47). Chebyshev polynomials and K-Nearest Neighbours (KNN) classifier was evaluated on Accuracy of PVC detection. The proposed method based on Chebyshev polynomial coefficients and a KNN classifier achieved 99.35% accuracy, 99.86% sensitivity, and 85.11% specificity in detecting premature ventricular contractions.
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