Arrhythmia classification from wavelet feature on FGPA

• Main Authors: Jatmiko, W., Mursanto, P., Febrian, A., Fajar, M., Anggoro, W. T., Rambe, R. S., Tawakal, M. I., Jovan, F. F., Eka S, M.
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Discrete wavelet transforms; Electrocardiography; Feature extraction; Field programmable gate arrays; Random access memory; Wavelet analysis
• DOI: 10.1109/MHS.2011.6102207

Arrhythmia is a condition when heart beats are not beating properly, either in rhythm or in intensity. Sometimes arrhythmia problems could make patients in dangerous condition due to their sortie. However, good classification and diagnostic in arrhythmia will help many lives from fatal menace. Many different diagnostics and classifications have been conducted recently by using neural network as their classifier, both in simulation and real hardware implementation. Nevertheless, the products as an arrhythmia classifier are not small enough for daily use. Our previous research [3] succeeded making a simulation for heart beats classifier on neural network. In this research, we tried to implement it on a prototype small arrhythmia classifier on FPGA using Spartan 3AN development board.