The proposed k-nearest neighbor algorithm with principal component analysis achieved a classification accuracy of 99.63%, sensitivity of 99.29%, and specificity of 99.89% for detecting premature ventricular contractions.
Cardiac arrhythmia is one of the most important indicators of heart disease. Premature ventricular contractions (PVCs) are a common form of cardiac arrhythmia caused by ectopic heartbeats. The detection of PVCs by means of ECG (electrocardiogram) signals is important for the prediction of possible heart failure. This study focuses on the classification of PVC heartbeats from ECG signals and, in particular, on the performance evaluation of time series approaches to the classification of PVC abnormality. Moreover, the performance effects of several dimension reduction approaches were also tested. Experiments were carried out using well-known machine learning methods, including neural networks, k-nearest neighbour, decision trees, and support vector machines. Findings were expressed in terms of accuracy, sensitivity, specificity, and running time for the MIT-BIH Arrhythmia Database. Among the different classification algorithms, the k-NN algorithm achieved the best classification rate. The results demonstrated that the proposed model exhibited higher accuracy rates than those of other works on this topic. According to the experimental results, the proposed approach achieved classification accuracy, sensitivity, and specificity rates of 99.63%, 99.29% and 99.89%, respectively.
Kaya et al. (Thu,) conducted a other in Premature Ventricular Contraction (n=7,000). k-Nearest Neighbor (k-NN) algorithm with Principal Component Analysis (PCA) vs. Neural Networks, Support Vector Machines, and Decision Trees was evaluated on Classification accuracy. The proposed k-nearest neighbor algorithm with principal component analysis achieved a classification accuracy of 99.63%, sensitivity of 99.29%, and specificity of 99.89% for detecting premature ventricular contractions.