Efficient Binary Classification of Electrocardiogram Signals through Machine Learning Techniques

Abstract

Doctors prefers different tests to discover abnormalities in different parts of a human body.   Electrocardiogram (ECG) tests are used by doctors to determine abnormal heart rhythm(arrhythmias). ECG test identifies arteries in the heart causing pain or a heart attack, whether the person had a previous heart attack, etc. Heart diseases are identified based on different signals discovered in ECG scan. The ECG record the electrical activity of the heart, where each heart beat is displayed as a series electrical wave. Efficient classification of ECG signals plays vital role in heart disease detection. In this paper, ECG signals are classified through machine learning techniques for improving the rate of accuracy for disease identification.