Diagnosis of cardiac arrhythmia using Swarm-intelligence based Metaheuristic Techniques: A comparative analysis

Abstract

INTRODUCTION: Heart diseases are the prominent human disorders that have significantly affected the lifestyle and lives of the victims. Cardiac arrhythmia (heart arrhythmia) is one of the critical heart disorders that reflects the state ofheartbeat among individuals. ECG (Electrocardiogram) signals are commonly used in the diagnostic process of this cardiac disorder.

OBJECTIVES: In this manuscript, an effort has been made to employ and examine the performance of emerging Swarm Intelligence (SI) techniques in finding an optimal set of features used for cardiac arrhythmia diagnosis.

METHODS: A standard benchmark UCI dataset set comprises of 279 attributes and 452 instances have been considered. Five different SI-based meta-heuristic techniques viz. binary Grey-Wolf Optimizer (bGWO), Ant Lion Optimization(ALO), Butterfly optimization algorithm (BOA), Dragonfly Algorithm (DA), and Satin-B ird Optimization(SBO) have been also employed for the same. Additionally, five novel chaotic variants of SBO have been designed to solve the feature selection problem for diagnosing a cardiac arrhythmia. Different performance metrics like accuracy, fitness value, optimal set of features and execution time have been computed.

CONCLUSION: It has been observed from the experimentation that in terms of accuracy and fitness value of cardiac arrhythmia, the SBO outperformed other SI algorithms viz. bGWO, DA, BOA, and ALO. Additionally, BOA and ALO seem to be the best fit when the emphasis is on dimension size only.