ELECTROCARDIOGRAM ARRHYTHMIA CLASSIFICATION SYSTEM USING SUPPORT VECTOR MACHINE BASED FUZZY LOGIC

  • Sugiyanto Sugiyanto Department of Informatics Engineering, Faculty of Information Technology, Adhi Tama Institute of Technology Surabaya
  • Tutuk Indriyani Department of Informatics Engineering, Faculty of Information Technology, Adhi Tama Institute of Technology Surabaya
  • Muhammad Heru Firmansyah Department of Informatics Engineering, Faculty of Information Technology, Adhi Tama Institute of Technology Surabaya
Keywords: arrhythmia classification, ECG, fuzzy logic, heart rate, Support Vector Machine

Abstract

Arrhythmia is a cardiovascular disease that can be diagnosed by doctors using an electrocardiogram (ECG). The information contained on the ECG is used by doctors to analyze the electrical activity of the heart and determine the type of arrhythmia suffered by the patient. In this study, ECG arrhythmia classification process was performed using Support Vector Machine based fuzzy logic. In the proposed method, fuzzy membership functions are used to cope with data that are not classifiable in the method of Support Vector Machine (SVM) one-against-one. An early stage of the data processing is the baseline wander removal process on the original ECG signal using Transformation Wavelet Discrete (TWD). Afterwards then the ECG signal is cleaned from the baseline wander segmented into units beat. The next stage is to look for six features of the beat. Every single beat is classified using SVM method based fuzzy logic. Results from this study show that ECG arrhythmia classification using proposed method (SVM based fuzzy logic) gives better results than original SVM method. ECG arrhythmia classification using SVM method based fuzzy logic forms an average value of accuracy level, sensitivity level, and specificity level of 93.5%, 93.5%, and 98.7% respectively. ECG arrhythmia classification using only SVM method forms an average value accuracy level, sensitivity level, and specificity level of 91.83%, 91.83%, and 98.36% respectively.

Published
2016-02-15