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

Yıl 2018, Cilt: 6 Sayı: 2, 83 - 87, 30.04.2018

Baris Karakaya , Turgay Kaya , Arif Gulten

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

Cited By: 7

Öz

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.

Anahtar Kelimeler

ANN, EEG, FPGA, Epilepsy

Kaynakça

• [1] J. Gotman, “Automatic recognition of epileptic seizures in the EEG”, Electroencephalography and Clinical Neurophysiology, Vol. 54, No. 5, 1982, pp. 530-540. [2] D., Gür, T. Kaya, M. Türk, “Analysis of Normal and Epileptic EEG Signals with Filtering Methods”, 2014 IEEE 22nd Signal Processing and Communications Applications Conference, SIU 2014, pp. 1877-1880. [3] S. Güzel, T. Kaya, H. Güler, “LabVIEW-Based Analysis of EEG Signals in Determination of Sleep Stages”, 2015 IEEE 23nd Signal Processing and Communications Applications Conference, SIU 2015, 2015. [4] H. Adeli, S. Ghosh-Dastidar, N. Dadmehr, “A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy”, IEEE Trans. Biomed. Eng., Vol. 54, 2007, pp. 205-211. [5] M.M. Shaker, “EEG Waves Classifier using Wavelet Transform and Fourier Transform”, Journal of Medical, Pharmaceutical Science and Engineering, Vol. 1, No. 3, 2007. [6] T. Kaya, M.C. Ince, “Design of FIR Filter Using Modeled Window Function with Helping of Artificial Neural Networks”, Journal of The Faculty of Engineering and Architecture of Gazi University, Vol. 27, No. 3, 2012, 599-606. [7] R. Schuyler, A. White, K. Staley, K.J. Cios, “Epileptic seizure detection”, IEEE Eng. Med. Biol. Mag., 2007, pp. 74-81. [8] A. Ersöz, S. Özşen, “Uyku EEG Sinyalinin Yapay Sinir Ağ Modeli ile Sınıflandırılması”, Elektrik Elektronik Bilgisayar Sempozyumu, Elazığ, 2011. [9] S.M. Eka, M. Fajar, T. Iqbal, W. Jatmiko, I.M. Agus, “FNGLVQ FPGA design for sleep stages classification based on electrocardiogram signal”, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012, pp. 2711-2716. [10] A.G. Blaiech, K. Ben Khalifa, M. Boubaker, M.H. Bedoui, “Multi-width fixed-point coding based on reprogrammable hardware implementation of a multi-layer perceptron neural network for alertness classification”, 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010, pp. 610-614. [11] American Clinical Neurophysiology Society, “Guideline 8: guidelines for recording clinical EEG on digital media”, J Clin Neurophysiol, Vol. 23, No. 2, 2006, pp. 122–124. [12] B. Karakaya, R. Yeniceri, M.E. Yalcın, “Wave computer core using fixed-point arithmetic”, 2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015, pp. 1514-1517. [13] E. Özpolat, B. Karakaya, T. Kaya, A. Gülten, “FPGA-based digital Filter Design for Biomedical Signal”, 2016 XII International Conference on Perspective Technologies and Methods in MEMs Design (MEMSTECH), 2016, pp. 70-73.