FPGA-based ANN Design for Detecting Epileptic Seizure in EEG Signal

Year 2018, Volume: 6 Issue: 2, 83 - 87, 30.04.2018

Baris Karakaya , Turgay Kaya , Arif Gulten

https://doi.org/10.17694/bajece.419544

Cited By: 7

Abstract

This study aims to represent an FPGA (Field
Programmable Gate Array) design of Artificial Neural Network (ANN) for
Electroencephalography (EEG) signal processing in order to detect epileptic
seizure. For analyzing brain’s electrical activity, feedforward ANN model is
used for classification of EEG signals. The designed ANN output layer makes a
decision whether the person has epilepsy or not. In the proposed system, the
ANN model is programmed and simulated on Xilinx ISE editor via computer and
then, EEG signal data are transferred to FPGA-based ANN emulator core. The Core
is trained on data which are patient’s data and healthy person’s data. After
training, test data is loaded to ANN Emulator Core to detect any epileptic seizure
of person’s EEG signal. The main advantage of FPGA in the system is to improve
speed and accuracy for epileptic seizure detection.

Keywords

ANN, EEG, FPGA, Epilepsy

References

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