Evaluation of window size in classification of epileptic short-term EEG signals using a Brain Computer Interface software

K. D. Tzimourta, L. G. Astrakas, A. M. Gianni, A. T. Tzallas, N. Giannakeas, I. Paliokas, D. G. Tsalikakis, M. G. Tsipouras

Abstract


The complexity of epilepsy created a fertile ground for further research in automated methods, attempting to help the epileptologists’ task. Over the past years, great breakthroughs have emerged in computer-aided analysis and the advent of Brain Computer Interface (BCI) systems has greatly facilitated the automated seizure analysis. In this study, an evaluation of the window size in automated seizure detection is proposed. The EEG signals from the University of Bonn was employed and segmented into 24 epochs of different window lengths with 50% overlap each time. Statistical and spectral features were extracted in the OpenViBE scenario that were used to train four different classifiers. Results in terms of accuracy were above 80% for the Decision Trees classifier. Also, results indicated that different window sizes provide small variations in classification accuracy.

Keywords


Epilepsy; EEG; seizure detection; window size; Brain Computer Interface

Full Text:

PDF

References


World Health Organization, “Epilepsy,” Fact sheet N999, [Online]. 2012. [Retrieved Feb. 2017] Available: http://www.who.int/mediacentre/

factsheets/fs999/en/

O. Devinsky, T. Spruill, D. Thurman, D. Friedman.. “Recognizing and preventing epilepsy-related mortality A call for action”, Neurology, Vol. 86, No. 8, pp. 779-786, 2016

I. E. Scheffer, S. Berkovic, G. Capovilla, M. B. Connolly, J. French, L. Guilhoto, L., ... and D. R. Nordli, “ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classification and Terminology”, Epilepsia, Vol. 58, No. 4, pp. 512-521, 2017

S. Segkouli, I. Paliokas, D. Tzovaras, M. Tsolaki, C. Karagiannidis, “Study of EEG Power Fluctuations Enhanced by Linguistic Stimulus for Cognitive Decline Screening”, International Symposium on Pervasive Computing Paradigms for Mental Health. pp. 165-175. Springer, Cham, 2015

A. T. Tzallas, M. G. Tsipouras, D. G. Tsalikakis, E. C. Karvounis, L. Astrakas, S. Konitsiotis, M. Tzaphlidou. “Automated epileptic seizure detection methods: a review study”, Epilepsy-histological, electroencephalographic and psychological aspects [Online], 2012.

I, Guler, I., and E.D. Ubeyli, “Multiclass support vector machines for EEG-signals classification”, IEEE Transactions on Information Technology in Biomedicine, Vol. 11, No. 2, pp. 117-126, 2007

Y. Kumar, M.L. Dewal, R.S. Anand, “Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine”, Neurocomputing, Vol. 133, pp. 271-279, 2014.

K.D. Tzimourta, L. G. Astrakas, M. G. Tsipouras, N. Giannakeas, A. T. Tzallas S. Konitsiotis, “Wavelet based classification of epileptic seizures in EEG signals”, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 35-39, 2017

A. T. Tzallas, M. G. Tsipouras, D. I. Fotiadis, “Automatic seizure detection based on time-frequency analysis and artificial neural networks”, Computational Intelligence and Neuroscience, Vol. 18, pp. 1-13, 2007

S. S. Alam and M.I.H. Bhuiyan, “Detection of seizure and epilepsy using higher order statistics in the EMD domain”, IEEE journal of biomedical and health informatics, Vol. 17, No. 2, pp. 312-318, 2013

A. Bhardwaj, A. Tiwari, R. Krishna, V. Varma. “A novel genetic programming approach for epileptic seizure detection”, Computer methods and programs in biomedicine. Vol. 124, pp. 2-18, 2016

L. Huang, and G. van Luijtelaar, “Brain computer interface for epilepsy treatment”, Brain-Computer Interface Systems-Recent Progress and Future Prospects, Fazel-Rezai R., Eds.; InTech Chapter Publisher, London, UK, 2013

Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby, V. Delannoy, O. Bertrand, A. Lécuyer, “OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments1”. Presence: teleoperators and virtual environments. Vol. 19, 2010

R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C. E. Elger. “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state”, Physical Review E, Vol. 64, No. 6, 061907. 2001

R.O. Duda, P. E. Hart D. G. Stork.. Pattern classification. John Wiley & Sons. 2012

S. Xie and S. Krishnan., “Dynamic principal component analysis with nonoverlapping moving window and its applications to epileptic EEG classification”, The Scientific World Journal, 2014

N. Seddik, S. Youssef, M. Kholief, “Automatic seizure detection in long-term scalp EEG using Weighted Permutation Entropy and Support Vector Machine”, Biomedical Engineering Conference (CIBEC), pp. 170-173, 2014

N. S. Tawfik, S. M. Youssef, M. Kholief, “A hybrid automated detection of epileptic seizures in EEG records”, Computers & Electrical Engineering, Vol. 53, pp. 177-190, 2014

V. Nagaraj, S. Lee, E. Krook-Magnuson, I. Soltesz, P. Benquet, P. Irazoqui, T. Netoff, “The Future of Seizure Pre-diction and Intervention: Closing the loop”, Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society, Vol. 32, No. 3, pp. 194, 2015




eISSN: 1792-8036     pISSN: 2241-4487