Daftar Isi: Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Main Authors:	Jatmiko, Wisnu, Setiawan, I Md. Agus, Akbar, Muhammad Ali, Suryana, Muhammad Eka, Wardhana, Yulistiyan, Rachmadi, Muhammad Febrian
Format:	Book application/pdf Journal
Terbitan:	UI Scholars Hub , 2016
Subjects:	arrhythmia learning vector quantization FN-GLVQ FPGA Chemical Engineering Civil Engineering Computer Engineering Electrical and Electronics Materials Science and Engineering Mechanical Engineering Metallurgy Ocean Engineering Structural Engineering
Online Access:	https://scholarhub.ui.ac.id/mjt/vol20/iss2/5 https://scholarhub.ui.ac.id/cgi/viewcontent.cgi?article=1304&context=mjt

Daftar Isi:

Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.