Classification of Cardiac Arrhythmia of 12 Lead ECG Using Combination of SMOTEENN, XGBoost and Machine Learning Algorithms

Abstract

Cardiac Arrhythmia is one of those common diseases leading to severe health problems for patients and even sudden death in some cases. Early detection of arrhythmias has a great role in saving lives which can be achieved by analyzing and classifying ECG signal into one of the cardiac arrhythmia. This study gives a method to classify the arrhythmia patients have into one of ten classes, where one class represents the normal condition and the other classes represent various types of arrhythmia conditions. This dataset has been preprocessed. The dataset being highly unbalanced, a combination of oversampling and under sampling using SMOTEENN is applied and feature reduction is carried out using XGboost. The feature reduced dataset is then classified using different supervised learning algorithms of machine learning and an accuracy of 97.48% has occurred which is better than state of art method. This study can be further elaborated using real time data for classification.