ELECTROCARDIOGRAM ARRHYTHMIA CLASSIFICATION SYSTEM USING SUPPORT VECTOR MACHINE BASED FUZZY LOGIC
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.
Author: Sugiyanto, Tutuk
Indriyani, Muhammad Heru Firmansyah
Journal Code: jptkomputergg160004
