Abstract
A computational framework to support seizure predictions in epileptic patients is presented. It is based on mining and knowledge discovery in Electroencephalogram (EEG) signal. A set of features is extracted and classification techniques are then used to eventually derive an alarm signal predicting a coming seizure. The epileptic patient may then take steps in order to prevent accidents and social exposure.
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Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods 134, 9–21 (2004)
The MathWorks, Inc.
Esteller, R., Echauz, J., D’Alessandro, M., Worrell, G., et al.: Continuous energy variation during the seizure cycle: towards an on-line accumulated energy. Clinical Neurophysiology 116, 517–526 (2005)
Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., et al.: Epileptic seizures begin hours in advance of clinical onset: a report of five patients. Neuron 30, 51–64 (2001)
Wichard, J., Parlitz, U.: Applications of nearest neighbours statistics. In: International Symposium on Nonlinear Theory and Its Applications (NOLTA 1998) (1998)
Freiburger Zentrum fur Datenanalyse und mollbildung, http://www.fdm.uni-freiburg.de/groups/timeseries/epi/EEGData/download/infos.txt
Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., et al.: Epileptic seizures begin hours in advance of clinical onset: a report of five patients. Neuron 30, 51–64 (2001)
Gigola, S., Ortiz, F., D’Atellis, C., Silva, W., Kochen, S.: Prediction of epileptic seizures using accumulated energy in a multiresolution framework. Journal of Neuroscience Methods 138, 107–111 (2004)
Jirsch, J.D., Urrestarazu, E., LeVan, P., Olivier, A., Dubeau, F., Gotman, J.: High-frequency oscillations during human focal seizures. Brain129 (Pt 6), 593–608 (June 2006)
Winterhalder, M., Schelter, B., Maiwald, T., Brandt, A., Schad, A., Schulze-Bonhage, A., Timmer, J.: Spatio-temporal patient–individual assessment of synchronization changes for epileptic seizure prediction. Clinical Neurophysiology 117, 2399–2413 (2006)
Le van Quyen, M., Martinerie, J., Navarro, V., Boon, P., D’Have, M., Adam, C., et al.: Anticipation of epileptic seizures from standard EEG recordings. Lancet 357, 183–188 (2001b)
Lehnertz, K., Andrzejak, R., Arnhold, J., Kreuz, T., Mormann, F., Rieke, C., et al.: Nonlinear EEG analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation, and prevention. J. Clin. Neurophysiol. 18, 209–222 (2001)
Mormann, F., Kreuz, T., Rieke, C., Lehnertz, K., et al.: On the predictability of epileptic seizures. Clinical neurophysiology 116, 569–587 (2005)
Dourado, A., Ferreira, E., Barbeiro, P.: VISRED - Numerical Data Mining with Linear and Nonlinear Techniques. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol. 4597, pp. 92–106. Springer, Heidelberg (2007), http://eden.dei.uc.pt/~dourado
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Direito, B., Dourado, A., Sales, F., Vieira, M. (2008). An Application for Electroencephalogram Mining for Epileptic Seizure Prediction. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_7
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DOI: https://doi.org/10.1007/978-3-540-70720-2_7
Publisher Name: Springer, Berlin, Heidelberg
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