Sub-band Decomposition for Epileptic Seizure Prediction | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 4, Volume 28, Issue 2, July 2019, Page 53-64 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.62763 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Asmaa Hamad* 1; Taha Taha1; Sayed El-Rabaie1; Adel El-Fishawy1; Turky Alotaiby2; Saleh Alshebeili2; Fathi Abd El-Samie1 | ||||
| 1Dept. of Electrical Engineering, Faculty of Engineering, Menoufia University. | ||||
| 2Dept. of Electrical Engineering, King Suad University, Riydh. | ||||
| Abstract | ||||
| This paper presents a frame work for the segmentation of EEG signals into three distinctive patterns; normal, pre-ictal and ictal based on sub-band decomposition. The objective of this segmentation process is to implement it on a mobile connected wirelessly to the electrode headset in order to give audio or visual alarms to epilepsy patients in case of epileptic seizure approaching. EEG signals contain five bands; Delta, Theta, Alpha, Beta, and Gamma (δ, θ α, β, and γ). The study in this paper tests each sub-band for possibility of seizure prediction. The sub-band decomposition is performed with IIR filters. The prediction method adopts a statistical approach that has training and testing phases. The training phase comprises estimation of five signals attributes; amplitude, derivative, local mean, local variance, and median. The PDF of each attribute is estimated for normal and pre-ictal states. Based on pre-set prediction probability and false alarm probability constraint a process of channel selection and bin selection from the PDFs of the selected as a tool for feature reduction and selection. The testing phase is performed with a threshold strategy on the selected bins. A majority voting strategy with a moving average smoothing filters is used for decision making. Simulation results proved the feasibility of the gamma band for seizure prediction. | ||||
| References | ||||
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text-stroke-width: 0px; "> 1] K. Lehnertz, F. Mormann, T. Kreuz, R. G. Andrzejak, C. Rieke, P. David, and C. E. Elger, “Seizure prediction by nonlinear EEG analysis,” IEEE Engineering in Medicine and Biology Magazine, Vol.22, No.1, pp.57-63, January -February 2003. [2] T. N. Alotaiby, S. A. Alshebeili, T. Alshawi, I. Ahmad, and F. E. A. ElSamie, “EEG seizure detection and prediction algorithms: a survey,’’ EURASIP Journal on Advances in Signal Processing, No.1, pp.183, December 2014. [3] J. R. Williamson, D. W. Bliss, , D. W. Browne, and J. T. Narayanan, “Seizure prediction using EEG spatiotemporal correlation structure,” Epilepsy & Behavior, Vol.25, No.2, pp.230-238, October 2012.. [4] S. Li, W. Zhou, Q. Yuan, and Y. Liu, “Seizure prediction using spike rate of intracranial EEG,” IEEE transactions on neural systems and rehabilitation engineering, Vol.21, No.6, pp.880-886, November 2013. [5] K. Gadhoumi, J. M. Lina and J. Gotman, “Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity,” Clinical Neurophysiology, Vol.124, No.9, pp.1745-1754 , September 2013. [6] Y. Zhang, W. Zhou, Q. Yuan and Q. Wu, “A low computation cost method for seizure prediction,” Epilepsy research, Vol.108, No.8, pp.1357- 1366 October 2014. [7] https://en.wikipedia.org/wiki/Electroencephalography#cite_ref-55. (Access Date 15 October 2017) e-width: 0px; "> [8] Recommendations for the Practice of Clinical Neurophysiology: Guidelines of the International Federation of Clinical Physiology (EEG Suppl. 52) Editors: G. Deuschl and A. Eisen q 1999 International Federation of Clinical Neurophysiology. All rights reserved. Published by Elsevier Science B.V. [9] L.J. Karam, , J.H. McClellan, E.W. Selesnick, and C.S Burrus, “Digital signal processing handbook,” 1999. [10] https://en.wikipedia.org/wiki/Bilinear_transform. (Access Date 20 March 2017). [11] https://dsp.stackexchange.com/questions/22016/how-is-the-lowpass-tobandpass-transformation-derived. (Access Date 10 November 2017). [12] F. E. Abd El-Samie, “Information security for automatic speaker identification,” 1st Edition, Springer, 2011. [13] S. M. Kuo, B. H. Lee and W. Tian, “Real-time digital signal processing, implementations and applications,” John Wiley & Sons, 2006. [14] L. Yin, R. Yang, M. Gabbouj and Y. Neuvo, “Weighted median filters: A Tutorial,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, pp.157-192,1996. [15] http://physionet.org/pn6/chbmit/. | ||||
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