electrocardiogram arrhythmia classification system using support vector machine based fuzzy logic

sistem klasifikasi aritmia elektrokardiogram menggunakan logika fuzzy berbasis support vector machine

Clicks: 360
ID: 227288
2016
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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.
Aritmia merupakan penyakit kardiovaskular yang dapat didiagnosis oleh dokter menggunakan elektrokardiogram (EKG). Informasi yang terkandung dalam EKG digunakan oleh dokter untuk menganalisis aktivitas kelistrikan jantung dan menentukan jenis aritmia yang diderita oleh pasien. Dalam penelitian ini, proses klasifikasi aritmia EKG dilakukan dengan menggunakan *Support Vector Machine* berbasis logika *fuzzy*. Pada metode yang diusulkan, fungsi keanggotaan *fuzzy* digunakan untuk menangani data yang tidak dapat diklasifikasikan dalam metode *Support Vector Machine* (SVM) *one-against-one*. Tahap awal pemrosesan data adalah proses penghilangan *baseline wander* pada sinyal EKG asli menggunakan Transformasi Wavelet Diskrit (TWD). Setelah itu, sinyal EKG yang telah dibersihkan dari *baseline wander* disegmentasi ke dalam unit detak (*beat*). Tahap selanjutnya adalah mencari enam fitur dari detak tersebut. Setiap detak diklasifikasikan menggunakan metode SVM berbasis logika *fuzzy*. Hasil dari penelitian ini menunjukkan bahwa klasifikasi aritmia EKG menggunakan metode yang diusulkan (SVM berbasis logika *fuzzy*) memberikan hasil yang lebih baik daripada metode SVM asli. Klasifikasi aritmia EKG menggunakan metode SVM berbasis logika *fuzzy* menghasilkan nilai rata-rata tingkat akurasi, tingkat sensitivitas, dan tingkat spesifisitas masing-masing sebesar 93,5%, 93,5%, dan 98,7%. Sementara itu, klasifikasi aritmia EKG yang hanya menggunakan metode SVM menghasilkan nilai rata-rata tingkat akurasi, tingkat sensitivitas, dan tingkat spesifisitas masing-masing sebesar 91,83%, 91,83%, dan 98,36%.
Reference Key
sugiyanto2016jurnalelectrocardiogram Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Sugiyanto Sugiyanto;Tutuk Indriyani;Muhammad Heru Firmansyah
Journal turkish journal of chemistry
Year 2016
DOI
10.21609/jiki.v9i1.364
URL
http://jiki.cs.ui.ac.id/index.php/jiki/article/view/364
Keywords
computer science heart rate fuzzy logic

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