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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.

Bahasa Abstract

Deteksi Otomatis Detak Arrhythmia: Algoritma, Sistem dan Implementasi. Penyakit jantung adalah salah satu penyebab utama kematian di dunia. Diagnosa awal dari penyakit tersebut didasarkan pada ketidaknormalan pola detak jantung, atau disebut juga sebagai Arrhythmia. Sebuah metode baru bernama fuzzy neuro generalized learning vector quantization (FN-GLCQ) kami kembangkan untuk melakukan klasifikasi terhadap pola tersebut. Algoritma tersebut dimodifikasi dari algoritma GLVQ dengan mengimplementasikan konsep fuzzy logic sebagai fungsi diskriminan untuk meningkatkan performa klasifikasi. Algoritma tersebut dites dengan menggunakan MIT-BIH arrythmia database untuk dianalisis performanya. Berdasarkan uji coba yang telah dilakukan, FN-GLVQ dapat meningkatkan akurasi dari performa GLVQ. Sesuai dengan tujuan kami untuk membangun alat pendeteksian otomatis, FN-GLVQ juga diimplementasikan pada Field Gate Programmable Array sebagai prototipe dari perangkat yang akan dibangun.

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