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A Survey of ECG Classification for Arrhythmia Diagnoses Using SVM

  • Doshi AyushiEmail author
  • Bhatt Nikita
  • Shah Nitin
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)

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.

Keywords

ECG DWT SVM Arrhythmia Denoising technique 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringCSPIT CHARUSATChangaIndia
  2. 2.Department of Quality AssuranceTechsmith Solutions Pvt Ltd.Thaltej, AhmedabadIndia

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