A Survey of ECG Classification for Arrhythmia Diagnoses Using SVM

Abstract

For Detecting Arrhythmia, the commonly used Medical test is an Electrocardiogram (ECG) which is widely used by medical practitioners to measure the electrical activity of heart. By Analysing ECG signal’s each heart beat we can find the abnormalities present in heart rhythm. In this work we survey different methods used for classifying ECG arrhythmia using Support Vector Machine and also discussed about the challenges associated with the classification of ECG signal. For classification we require Pre-Processing of ECG signal, Preparation Method, Feature Extraction or Feature Selection Methods, Multi class classification strategy and kernel method for SVM classifier. Recently, for the classification we have several datasets available which have been clinically detected arrhythmia present in each ECG recordings. By initiating this research survey we aim to explore current methodology for diagnosing arrhythmia and classifying ECG signal using SVM.