Author/Authors :
Jatmiko, Wisnu university of indonesia - Faculty of Computer Science, Indonesia , Setiawan, I Md. Agus university of indonesia - Faculty of Computer Science, Indonesia , Akbar, Muhammad Ali university of indonesia - Faculty of Computer Science, Indonesia , Suryana, Muhammad Eka university of indonesia - Faculty of Computer Science, Indonesia , Wardhana, Yulistiyan university of indonesia - Faculty of Computer Science, Indonesia , Rachmadi, Muhammad Febrian University of Edinburgh - School of Informatics, UK
Abstract :
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.
Keywords :
arrhythmia , learning vector quantization , FN , GLVQ , FPGA
