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The Scientific World Journal
Volume 2014, Article ID 140863, 8 pages
http://dx.doi.org/10.1155/2014/140863
Research Article

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

1Department of Pediatrics, Peking University First Hospital, No. 1 of Xian Men Street, Xicheng District, Beijing 100034, China
2State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
3Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China

Received 13 December 2013; Accepted 18 March 2014; Published 25 March 2014

Academic Editors: J. Tang and H. Zhou

Copyright © 2014 Zhixian Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Patry, S. Lyagoubi, and C. A. Tassinari, “Subclinical “electrical status epilepticus” induced by sleep in children. A clinical and electroencephalographic study of six cases,” Archives of Neurology, vol. 24, no. 3, pp. 242–252, 1971. View at Google Scholar · View at Scopus
  2. C. A. Tassinari, G. Rubboli, L. Volpi et al., “Encephalopathy with electrical status epilepticus during slow sleep or ESES syndrome including the acquired aphasia,” Clinical Neurophysiology, vol. 111, supplement 2, pp. S94–S102, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. F. B. J. Scholtes, M. P. H. Hendriks, and W. O. Renier, “Cognitive deterioration and electrical status epilepticus during slow sleep,” Epilepsy and Behavior, vol. 6, no. 2, pp. 167–173, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Raha, U. Shah, and V. Udani, “Neurocognitive and neurobehavioral disabilities in epilepsy with electrical status epilepticus in slow sleep (ESES) and related syndromes,” Epilepsy and Behavior, vol. 25, pp. 381–385, 2012. View at Google Scholar
  5. M. Siniatchkin, K. Groening, J. Moehring et al., “Neuronal networks in children with continuous spikes and waves during slow sleep,” Brain, vol. 133, no. 9, pp. 2798–2813, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Buzsaki, Rhythms of the Brain, Oxford University Press, Oxford, UK, 2006.
  7. R. A. Sarkis and J. W. Lee, “Quantitative EEG in hospital encephalopathy: review and microstate analysis,” Journal of Clinical Neurophysiology, vol. 30, pp. 526–530, 2013. View at Google Scholar
  8. W. Klimesch, P. Sauseng, and S. Hanslmayr, “EEG alpha oscillations: the inhibition-timing hypothesis,” Brain Research Reviews, vol. 53, no. 1, pp. 63–88, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Scheltens-de Boer, “Guidelines for EEG in encephalopathy related to ESES/CSWS in children,” Epilepsia, vol. 50, no. 7, pp. 13–17, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. O. A. Rosso, A. Mendes, J. A. Rostas, M. Hunter, and P. Moscato, “Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity,” Journal of Neuroscience Methods, vol. 177, no. 2, pp. 461–468, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. O. A. Rosso, A. Mendes, R. Berretta, J. A. Rostas, M. Hunter, and P. Moscato, “Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity (II): a combinatorial optimization approach for electrode selection,” Journal of Neuroscience Methods, vol. 181, no. 2, pp. 257–267, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Rogowski, I. Gath, and E. Bental, “On the prediction of epileptic seizures,” Biological Cybernetics, vol. 42, no. 1, pp. 9–15, 1981. View at Google Scholar · View at Scopus
  13. L. D. Iasemidis, J. C. Sackellares, H. P. Zaveri, and W. J. Williams, “Phase space topography and the lyapunov exponent of electrocorticograms in partial seizures,” Brain Topography, vol. 2, no. 3, pp. 187–201, 1990. View at Publisher · View at Google Scholar · View at Scopus
  14. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and 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: Statistical, Nonlinear, and Soft Matter Physics, vol. 64, no. 6, Article ID 061907, 2001. View at Google Scholar · View at Scopus
  15. V. Navarro, J. Martinerie, M. Le van Quyen et al., “Seizure anticipation in human neocortical partial epilepsy,” Brain, vol. 125, no. 3, pp. 640–655, 2002. View at Google Scholar · View at Scopus
  16. M. Niknazar, S. R. Mousavi, S. Motaghi, A. Dehghani, B. V. Vahdat, and M. B. Shamsollahi, “A unified approach for detection of induced epileptic seizures in rats using ECoG signals,” Epilepsy and Behavior, vol. 27, pp. 355–364, 2013. View at Google Scholar
  17. R. Ferri, R. Chiaramonti, M. Elia, S. A. Musumeci, A. Ragazzoni, and C. J. Stam, “Nonlinear EEG analysis during sleep in premature and full-term newborns,” Clinical Neurophysiology, vol. 114, no. 7, pp. 1176–1180, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Gao, J. Hu, and W.-W. Tung, “Entropy measures for biological signal analyses,” Nonlinear Dynamics, vol. 68, pp. 431–444, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Paisansathan, M. D. Ozcan, Q. S. Khan, V. L. Baughman, and M. S. Ozcan, “Signal persistence of bispectral index and state entropy during surgical procedure under sedation,” The Scientific World Journal, vol. 2012, Article ID 272815, 5 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. J. W. Sleigh, D. A. Steyn-Ross, M. L. Steyn-Ross, C. Grant, and G. Ludbrook, “Cortical entropy changes with general anaesthesia: theory and experiment,” Physiological Measurement, vol. 25, no. 4, pp. 921–934, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate and sample entropy,” The American Journal of Physiology—Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039–H2049, 2000. View at Google Scholar · View at Scopus
  22. S. M. Pincus, “Approximate entropy as a measure of system complexity,” Proceedings of the National Academy of Sciences of the United States of America, vol. 88, pp. 2297–2301, 1991. View at Google Scholar
  23. D. Abásolo, R. Hornero, P. Espino, J. Poza, C. I. Sánchez, and R. de la Rosa, “Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy,” Clinical Neurophysiology, vol. 116, no. 8, pp. 1826–1834, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Abásolo, R. Hornero, P. Espino, D. Álvarez, and J. Poza, “Entropy analysis of the EEG background activity in Alzheimer's disease patients,” Physiological Measurement, vol. 27, no. 3, pp. 241–253, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. N. Burioka, G. Cornélissen, Y. Maegaki et al., “Approximate entropy of the electroencephalogram in healthy awake subjects and absence epilepsy patients,” Clinical EEG and Neuroscience, vol. 36, no. 3, pp. 188–193, 2005. View at Google Scholar · View at Scopus
  26. N. Kannathal, M. L. Choo, U. R. Acharya, and P. K. Sadasivan, “Entropies for detection of epilepsy in EEG,” Computer Methods and Programs in Biomedicine, vol. 80, no. 3, pp. 187–194, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. U. R. Acharya, F. Molinari, S. V. Sree, S. Chattopadhyay, K.-H. Ng, and J. S. Suri, “Automated diagnosis of epileptic EEG using entropies,” Biomedical Signal Processing and Control, vol. 7, pp. 401–408, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. Song, J. Crowcroft, and J. Zhang, “Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine,” Journal of Neuroscience Methods, vol. 210, pp. 132–146, 2012. View at Google Scholar
  29. C. Bandt and B. Pompe, “Permutation entropy: a natural complexity measure for time series,” Physical Review Letters, vol. 88, no. 17, Article ID 174102, 2002. View at Google Scholar · View at Scopus
  30. M. Zanin, L. Zunino, O. A. Rosso, and D. Papo, “Permutation entropy and its main biomedical and econophysics applications: a review,” Entropy, vol. 14, pp. 1553–1577, 2012. View at Google Scholar
  31. G. Ouyang, C. Dang, D. A. Richards, and X. Li, “Ordinal pattern based similarity analysis for EEG recordings,” Clinical Neurophysiology, vol. 121, no. 5, pp. 694–703, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Cao, W. W. Tung, J. B. Gao, V. A. Protopopescu, and L. M. Hively, “Detecting dynamical changes in time series using the permutation entropy,” Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 70, no. 4, Article ID 046217, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. X. Li, G. Ouyang, and D. A. Richards, “Predictability analysis of absence seizures with permutation entropy,” Epilepsy Research, vol. 77, no. 1, pp. 70–74, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. N. Nicolaou and J. Georgiou, “Detection of epileptic electroencephalogram based on permutation entropy and support vector machines,” Expert Systems with Applications, vol. 39, no. 1, pp. 202–209, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. A. A. Bruzzo, B. Gesierich, M. Santi, C. A. Tassinari, N. Birbaumer, and G. Rubboli, “Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. A preliminary study,” Neurological Sciences, vol. 29, no. 1, pp. 3–9, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. S. Baek, C. A. Tsai, and J. J. Chen, “Development of biomarker classifiers from high-dimensional data,” Briefings in Bioinformatics, vol. 10, no. 5, pp. 537–546, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. R. Palaniappan, K. Sundaraj, and N. U. Ahamed, “Machine learning in lung sound analysis: a systematic review,” Biocybernetics and Biomedical Engineering, vol. 33, pp. 129–135, 2013. View at Google Scholar
  38. H. Zhou, H. Hu, H. Liu, and J. Tang, “Classification of upper limb motion trajectories using shape features,” IEEE Transactions on Systems, Man and Cybernetics C: Applications and Reviews, vol. 42, pp. 970–982, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. Z. J. Ju, G. X. Ouyang, M. Wilamowska-Korsak, and H. H. Liu, “Surface EMG based hand manipulation identification via nonlinear feature extraction and classification,” IEEE Sensors Journal, vol. 13, pp. 3302–3311, 2013. View at Google Scholar
  40. A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Systems with Applications, vol. 39, no. 8, pp. 7420–7431, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. U. R. Acharya, S. V. Sree, G. Swapna, R. J. Martis, and J. S. Suri, “Automated EEG analysis of epilepsy: a review,” Knowledge-Based Systems, vol. 45, pp. 147–165, 2013. View at Google Scholar
  42. M. Besserve, K. Jerbi, F. Laurent, S. Baillet, J. Martinerie, and L. Garnero, “Classification methods for ongoing EEG and MEG signals,” Biological Research, vol. 40, no. 4, pp. 415–437, 2007. View at Google Scholar · View at Scopus
  43. D. Nauck and R. Kruse, “Obtaining interpretable fuzzy classification rules from medical data,” Artificial Intelligence in Medicine, vol. 16, no. 2, pp. 149–169, 1999. View at Publisher · View at Google Scholar · View at Scopus
  44. L. I. Kuncheva and F. Steimann, “Fuzzy diagnosis: editorial,” Artificial Intelligence in Medicine, vol. 16, no. 2, pp. 121–128, 1999. View at Publisher · View at Google Scholar · View at Scopus
  45. I. Güler and E. D. Übeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,” Journal of Neuroscience Methods, vol. 148, no. 2, pp. 113–121, 2005. View at Publisher · View at Google Scholar · View at Scopus
  46. E. D. Übeyli, “Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents,” Expert Systems with Applications, vol. 36, no. 5, pp. 9031–9038, 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. A. Yildiz, M. Akin, M. Poyraz, and G. Kirbas, “Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction,” Expert Systems with Applications, vol. 36, no. 4, pp. 7390–7399, 2009. View at Publisher · View at Google Scholar · View at Scopus
  48. S. M. Pincus, I. M. Gladstone, and R. A. Ehrenkranz, “A regularity statistic for medical data analysis,” Journal of Clinical Monitoring, vol. 7, no. 4, pp. 335–345, 1991. View at Publisher · View at Google Scholar · View at Scopus
  49. S. M. Pincus, “Approximate entropy as a measure of irregularity for psychiatric serial metrics,” Bipolar Disorders I, vol. 8, no. 5, pp. 430–440, 2006. View at Publisher · View at Google Scholar · View at Scopus
  50. J. Bruhn, H. Ropcke, B. Rehberg, T. Bouillon, and A. Hoeft, “Electroencephalogram approximate entropy correctly classifies the occurrence of burst suppression pattern as increasing anesthetic drug effect,” Anesthesiology, vol. 93, no. 4, pp. 981–985, 2000. View at Google Scholar · View at Scopus
  51. D. E. Lake, J. S. Richman, M. Pamela Griffin, and J. Randall Moorman, “Sample entropy analysis of neonatal heart rate variability,” The American Journal of Physiology—Regulatory Integrative and Comparative Physiology, vol. 283, no. 3, pp. R789–R797, 2002. View at Google Scholar · View at Scopus
  52. S. M. Pincus and A. L. Goldberger, “Physiological time-series analysis: what does regularity quantify?” The American Journal of Physiology—Heart and Circulatory Physiology, vol. 266, no. 4, pp. H1643–H1656, 1994. View at Google Scholar · View at Scopus
  53. G. Ouyang, X. Li, C. Dang, and D. A. Richards, “Deterministic dynamics of neural activity during absence seizures in rats,” Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 79, Article ID 041146, 2009. View at Google Scholar
  54. M. Staniek and K. Lehnertz, “Parameter selection for permutation entropy measurements,” International Journal of Bifurcation and Chaos, vol. 17, no. 10, pp. 3729–3733, 2007. View at Publisher · View at Google Scholar · View at Scopus
  55. X. Li and G. Ouyang, “Estimating coupling direction between neuronal populations with permutation conditional mutual information,” NeuroImage, vol. 52, no. 2, pp. 497–507, 2010. View at Publisher · View at Google Scholar · View at Scopus
  56. J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993. View at Publisher · View at Google Scholar · View at Scopus