Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Calcium Channel Parameters With KNN And ANN Classifier

Abstract

Aim: Aim of this research is to analyze and compare ventricular cardiac arrhythmia classification using calcium channel parameters with Artificial Neural Network (ANN) and K- Nearest Neighbour (KNN) classifier. Materials and Methods: For the classification of arrhythmias, A.V.Panifilov (AVP) is used. THVCM contains well defined Calcium channel dynamics and its properties. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier such as K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifiers to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Results: The results obtained from Normal, Tachycardia and Bradycardia data are imported to the ANN and KNN classifier. In which KNN shows accuracy value (12.3950%), standard deviation (0.96490) and Standard error mean (0.21576). And ANN shows accuracy value (35.3400%), standard deviation (3.22285) and Standard error mean (0.72065). Conclusion: From the results, it is concluded that ANN produces better results when compared with KNN classification in terms of accuracy.