Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation - Jatmiko, Wisnu; Setiawan, I Md. Agus; Akbar, Muhammad Ali; Suryana, Muhammad Eka; Wardhana, Yulistiyan; Rachmadi, Muhammad Febrian (UI Scholars Hub, 2016)
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.