Epileptic Seizure Prediction and the Dimensionality Reduction Problem
Conference paper
Abstract
Seizures prediction may substantially improve the quality of life of epileptic patients. Processing EEG signals, by extracting a convenient set of features, is the most promising way to classify the brain state and to predict with some antecedence its evolution to a seizure condition. In this work neural networks are proposed as effective classifiers of brain state among 4 classes: interictal, preictal, ictal and postictal. A two channels set of 26 features is extracted. By correlation analysis and by extracting the principal components, a reduced features space is obtained where, by an appropriate neural network, over 90% successful classifications are achieved, for dataset with several patients from the Freiburg database.
Keywords
Classification Neural Networks Feature Selection PCA Correlation Epilepsy Seizure Prediction EEG ProcessingPreview
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References
- 1.National Institute of Neurological Disorders and Stroke, http://www.ninds.nih.gov/disorders/epilepsy/epilepsy.htm
- 2.Wyllie, E.: Epilepsy - Information for You and Those Who Care About You. Cleveland Press (2008)Google Scholar
- 3.People against childhood epilepsy, http://www.paceusa.org/indexframe5.html
- 4.Swiderski, B., Osowski, S., Cichocki, A., Rysz, A.: Epileptic Seizure Prediction Using Lyapunov Exponents and Support Vector Machine. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 373–381. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 5.Direito, B., Dourado, A., Vieira, M., Sales, M.: Combining energy and wavelet transform for epilectic seizure prediction in an advanced computational system. In: Proc. of the 2008 International Conference on Biomedical Engineering and Informatics BMEI2008, vol. 2, pp. 380–385, International Conference on Biomedical Engineering and informatics, Hainan, PR China (2008)Google Scholar
- 6.Mormann, F., Kreuz, T., Rieke, C., Andrzejak, R.G., Kraskov, A., David, P., Elger, C.E., Lehnertz, K.: On the predictability of epilectic seizures. Clinical Neurophysiology 116(3), 569–587 (2005)CrossRefGoogle Scholar
- 7.Jerger, K.K., Netoff, T.I., Francis, J.T., Sauer, T., Pecora, L., Weinstein, S.L., Schiff, S.J.: Early Seizure Detection. Journal of Cinical Neurophysiology 18, 259–269 (2001)CrossRefGoogle Scholar
- 8.Ahmad, T., Fairuz, R.A., Zakaria, F., Isa, H.: Selection of a Subset of EEG Channels of Epileptic Patient During Seizure Using PCA. In: Mathematics and Computers in Science, Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation, Cambridge, UK, pp. 270–273 (2008) ISBN ISSN: 1790-5117, 978-960-6766-44-2Google Scholar
- 9.Hu, S., Stead, M., Worrel, G.: Removal of Scalp Reference Signal and Line Noise for Intracranial EEGs. In: IEEE International Conference on Networking, Sensing and Control, Sanya, China, pp. 1486–1491 (2008) ISBN: 978-1-4244-1685-1Google Scholar
- 10.Schelter, B., Winterhalder, M., Maiwald, T., Brandt, A., Schad, A., Timmer, J., Schulze-Bonhage, A.: Do false predictions of seizures depend on the state of vigilance? A report from two seizure prediction methods and proposed remedies. Epilepsia 47(12), 2058–2070 (2006)CrossRefzbMATHGoogle Scholar
- 11.The Mathworks, Inc. Natick, MAGoogle Scholar
- 12.Merkwirth, C., Parlitz, U., Wedekind, I., Lauterborn, W.: TSTOOL User Manual, Version 1.11., http://www.dpi.physik.uni-goettingen.de/tstool/HTML/index.html
- 13.Esteller, R., Echauz, J., DAlessandro, M., Worrell, G., et al.: Continuous Energy Variation During the Seizure Cycle: Towards an On-line Accumulated Energy. Clinical Neurophysiology 116(3), 517–526 (2005)CrossRefGoogle Scholar
- 14.Mormann, F., Andrzejak, R.G., Elger, C.E., Lehnertz, K.: Seizure prediction: the long and winding road. Brain 130(2), 314–333 (2007)CrossRefGoogle Scholar
- 15.D’Alessandro, M., Vachtsevanos, G., Hinson, A., Esteller, R., Echauz, J., Litt, B.: A Genetic Approach to Selecting the Optimal Feature for Epileptic Seizure Prediction. In: IEEE International Conference on Engineering, Medicine and Biology, vol. 2, pp. 1703–1706 (2001) ISSN: 1094-687XGoogle Scholar
- 16.Schlens, J.: A Tutorial on Principal Component Analysis, http://www.cs.cmu.edu/~elaw/papers/pca.pdf
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