Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Cardiac disease is one of the major causes of death in e world. Early diagnose of the symptoms depen ds on abnormality on heart beat pattern, known as Arrhyth mia. A novel fuzzy neuro generalized learning vecto r quantization for automatic Arrhythmia heart beat classification s proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discrimin ant 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 Progra mmable Array to prototype the system into a real device.