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

Abstract

Aim: The Motive of this research is to analyze, compare ventricular Cardiac Arrhythmia (CA) classification using potassium channel (k+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers. Materials and Methods: D Noble Model For Human Ventricular Tissue (DNFHVT) is used for our classification. The DNFHVT is a mathematical model of action potential focusing on major ionic currents like K+,Na+ and Ca+.. Size of the sample was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20. These data are imported to KNN and ANN classifiers to find better accuracy among them. The accuracy of novel ANN and KNN classifiers for 20 samples is obtained by alternating the cross fold validation. These results will be imported to Statistical Package for the Social Science (SPSS) software to identify the overall accuracy for each classifier. Results: The results are obtained from SPSS for novel ANN and KNN classifiers. ANN shows accuracy of 13.14% with standard deviation (1.6800) and Standard error mean (0.3757). Similarly KNN produces an accuracy value of 7.19% with standard deviation (1.6902) and Standard error mean (0.377). Conclusion: As of the results, it clearly shows that ANN has better accuracy for classification than KNN.