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Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System

International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 113 - Number 2
Year of Publication: 2015
Hamid Niknazar
Keivan Maghooli
Ali Motie Nasrabadi

Hamid Niknazar, Keivan Maghooli and Ali Motie Nasrabadi. Article: Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System. International Journal of Computer Applications 113(2):24-30, March 2015. Full text available. BibTeX

	author = {Hamid Niknazar and Keivan Maghooli and Ali Motie Nasrabadi},
	title = {Article: Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {113},
	number = {2},
	pages = {24-30},
	month = {March},
	note = {Full text available}


Epileptic seizures are generated by abnormal activity of neurons. EEG-based epileptic seizure prediction could be a key to improve life style of patients that suffer from drug-resistance epilepsy. In this study, we propose a fuzzy logic system to predict epileptic seizures by using statistical behavior of local extrema (SBLE) features and a rule-based fuzzy system. Two approaches are considered to evaluate the proposed method. First approach is patient-dependent, which requires EEG data in preictal and interictal state. Second approach is leave one out (LOO) technique to evaluate generalizability of the method. Applied to the Freiburg EEG dataset, it was found that the method has good performance for most of the patients of this database. In the patient-dependent approach, sensitivity of 84% with no false alarm and sensitivity of 94. 15% with a false alarm rate of 0. 1 were achieved. LOO evaluation approach obtained a sensitivity of 79. 38% with a false alarm rate of 0. 049. It is remarkable that for many of patients, the proposed method achieved sensitivity of 100% with no false alarm in both of evaluation approaches. This study showed that application of SBLE features as inputs of fuzzy logic system is a suitable way to track EEG changes leading to epileptic seizures.


  • S. Shorvon, Handbook of epilepsy treatment, Blackwell Pub, 2006.
  • E. Reynolds, R. Elwes and S. Shorvon, "Why does epilepsy become intractable?: prevention of chronic epilepsy," The Lancet, vol. 322, pp. 925-954, 1983.
  • F. Mormann, R. Andrzejak, C. Elger and K. Lehnertz, "Seizure prediction: the long and winding road," Brain, vol. 130, pp. 314-333, 2007.
  • C. Elger and D. Schmidt, "Modern management of epilepsy: A practical approach," Epilepsy & Behavior, vol. 12, pp. 501-539, 2008.
  • D. Kugiumtzis and P. Larsson, "Linear and nonlinear analysis of EEG for the prediction of epileptic seizures," in Proceeding of the 1999 Workshop "Chaos in Brain?", Singapore, 2000.
  • S. Viglione and G. Wlsh, "Proceedings: Epileptic seizure prediction," Electroencephalogr Clin Neurophysiol, vol. 39, pp. 435-436, 1975.
  • Z. Rogowski, I. Gath and E. Bental, "On the prediction of epileptic seizures," Biol Cybern, vol. 42, pp. 9-15, 1981.
  • Y. Salant, I. Gath and O. Henriksen, "Prediction of epileptic seizures from two-channel EEG," Med Biol Eng Comput, vol. 36, pp. 549-556, 1998.
  • L. Iasemidis, J. Sackellares, H. Zaveri and W. Williams, "Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures," Brain Topogr, vol. 2, pp. 187-201, 1990.
  • R. Costa, P. Oliveira, G. Rodrigues, B. Leitao and A. Dourado, "Epileptic seizure classification using neural networks with 14 features," pp. 281-288, 2008.
  • "Freiburg seizure prediction database," 2007. [Online]. Available: http://epilepsy. uni-freiburg. de/freiburg-seizure-prediction-project/eeg-database.
  • N. Moghim and D. W. Corne, "Predicting Epileptic Seizures in Advance," PLoS ONE, vol. 9(6), p. e99334, 2014.
  • P. Ghaderyan, A. Abbasi and M. Sedaaghi, "An efficient seizure prediction method using KNN-based undersampling and linear frequency measures," Journal of Neuroscience Methods, 2014.
  • F. Mormann, T. Kreuz, C. Rieke, R. Andrzejak, A. Kraskov, P. David, C. Elger and K. Lehnertz, "On the predictability of epileptic seizures," Clin Neurophysiol, vol. 116, pp. 569-87, 2005.
  • Geva and D. Kerem, "Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering," IEEE Trans. Biomedical Engineering, vol. 45, pp. 1205-1216, 1998.
  • P. Mirowski, D. Madhavan, Y. LeCun and R. Kuzniecky, "Classification of patterns of EEG synchronization for seizure prediction," Clin Neurophysiol, vol. 120, pp. 1927-40, 2009.
  • Y. Park, L. Luo, K. Parhi and T. Netoff, "Seizure prediction with spectral power of EEG using cost-sensitive support vector machines," Epilepsia, vol. 52, pp. 1761-70, 2011.
  • Aarabi and B. He, "Seizure prediction in intracranial EEG: A patient-specific rule-based approach," in Med. Biol. Soc. , Boston, 2011.
  • Aarabi, R. Fazel-Rezai and Y. Aghakhani, "A fuzzy rule-based system for epileptic seizure detection in intracranial EEG," Clinical Neurophysiology, vol. 120, pp. 1648-1657, 2009.
  • Rabbi, L. Azinfar and R. Fazel-Rezai, "Seizure Prediction Using Adaptive Neuro-Fuzzy Inference System," in IEEE EMBS, Osaka, 2013.
  • T. Maiwald, M. Winterhalder, R. Aschenbrenner-Scheibe, H. Voss, A. Schulze-Bonhage and J. Timmer, "Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic," Physica D, vol. 194, pp. 357-368, 2004.
  • L. Tsoukalas and R. Uhrig, Fuzzy and Neural Approaches in Engineering, 1996.