Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Wisnu Jatmiko, I Md. Agus Setiawan, Muhammad Ali Akbar, Muhammad Eka Suryana, Yulistiyan Wardhana, Muhammad Febrian Rachmadi

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)82-92
JournalMakara Journal of Technology
Issue number2
Publication statusPublished - 1 Aug 2016


  • arrhythmia, learning vector quantization, FN-GLVQ, FPGA


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