CLASSIFICATION OF ECG BEATS USING MACHINE LEARNING TECHNIQUE

Abstract

According to the World Health Organization, cardiovascular diseases (CVD) are the main cause of death worldwide. An Estimated 17.5 million people died from CVD in 2012, 31% of all Representing Global deaths. The electrocardiogram (ECG) is a core tool for the pre-diagnosis of heart diseases. Many advances on ECG arrhythmia classification have Been developed in the last century; however, there is still research to identify malignant ECG waveforms on beats. The SVC complexes are known to be associated ventricular arrhythmias with malignant and in sudden cardiac death (SCD) cases. This Kind of detecting arrhythmia has been crucial in clinical applications. In this work, we extracted from 108,653 , 80 different features of the ECG beats classified MIT-BIH database in order to classify the Normal, SVC and other kind of ECG beats. with the help of supervised machine learning technique. In particular, we used supervised learning technique like Logistic Regression, Neural Network, KNN, Support vector machine, Random forest, Decision Tree. With the help of proposed method, we easily classify the ECG beat. From these algorithm Decision tree has the highest accuracy as 86.60%.