Performance Analysis for Arrhythmia Classification using PSO, GWO and SVM

Abstract

Proper heart rate or heart rhythm leads to healthy lifestyle. Improper heart rhythm means heartbeat will be sometimes too fast or too slow. Such preternatural condition of heart is named as Cardiac arrhythmia. Arrhythmia occurs when there is no proper working of electrical impulses present in the heart. An earlier detection of irregular heart rhythm is necessary in order to rescue ones survival. Classification of arrhythmia is needed for diagnosis. This paper confers the Principle component analysis as feature reduction process to reduce high dimensional input without influencing classification methods and two classification techniques such as Particle swarm optimization (PSO), Grey wolf optimizer (GWO) and Support Vector Machine (SVM). Performance Analysis for these three techniques is compared where it is used to classify various arrhythmias. The result explores the performance metrics for PSO, GWO, SVM and also integration of two methods such as PSO with SVM, GWO with SVO and shows that GWO integrated with SVM has 99.89% accuracy and performance better than other algorithms.