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Neurology Research International
Volume 2018, Article ID 8192820, 8 pages
https://doi.org/10.1155/2018/8192820
Research Article

Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?

1Department of Electronics and Communication Engineering, SCT College of Engineering, Thiruvananthapuram, Kerala, India
2Department of Neurology, SCTIMST, Thiruvananthapuram, Kerala, India
3Department of ECE, TKM College of Engineering, Kollam, Kerala, India
4Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, India
5Department of Anatomy, Government Medical College, Thiruvananthapuram, Kerala, India

Correspondence should be addressed to Jisu Elsa Jacob; moc.liamg@asleusij

Received 21 January 2018; Revised 5 April 2018; Accepted 23 April 2018; Published 29 May 2018

Academic Editor: Vincenzo Di Lazzaro

Copyright © 2018 Jisu Elsa Jacob 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. J. E. Jacob, V. S. Vijith, and K. Gopakumar, “Non linear analysis of epileptic EEG,” in Proceedings of the 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, pp. 392–396, IEEE, March 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, pp. 205–211, 2007. View at Publisher · View at Google Scholar
  3. N. Kannathal, U. R. Acharya, C. M. Lim, and P. K. Sadasivan, “Characterization of EEG—a comparative study,” Computer Methods and Programs in Biomedicine, vol. 80, no. 1, pp. 17–23, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. U. R. Acharya, C. K. Chua, T.-C. Lim, Dorithy, and J. S. Suri, “Automatic identification of epileptic EEG signals using nonlinear parameters,” Journal of Mechanics in Medicine and Biology, vol. 9, no. 4, pp. 539–553, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. J. E. Jacob, V. V. Sreelatha, T. Iype, G. K. Nair, and D. G. Yohannan, “Diagnosis of epilepsy from interictal EEGs based on chaotic and wavelet transformation,” Analog Integrated Circuits and Signal Processing, vol. 89, no. 1, pp. 131–138, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease,” Neuroscience Letters, vol. 444, no. 2, pp. 190–194, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Dauwels, F. Vialatte, and A. Cichocki, “Diagnosis of Alzheimer's disease from EEG signals: where are we standing?” Current Alzheimer Research, vol. 7, no. 6, pp. 487–505, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Jelles, J. H. Van Birgelen, J. P. J. Slaets, R. E. M. Hekster, E. J. Jonkman, and C. J. Stam, “Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls,” Clinical Neurophysiology, vol. 110, no. 7, pp. 1159–1167, 1999. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Li, S. Tong, D. Liu et al., “Abnormal EEG complexity in patients with schizophrenia and depression,” Clinical Neurophysiology, vol. 119, no. 6, pp. 1232–1241, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Natarajan, U. R. Acharya, F. Alias, T. Tiboleng, and S. K. Puthusserypady, “Nonlinear analysis of EEG signals at different mental states,” Biomedical Engineering Online, vol. 3, no. 1, article 7, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Fell, J. Röschke, K. Mann, and C. Schäffner, “Discrimination of sleep stages: A comparison between spectral and nonlinear EEG measures,” Electroencephalography and Clinical Neurophysiology, vol. 98, no. 5, pp. 401–410, 1996. View at Publisher · View at Google Scholar · View at Scopus
  12. V. Lalitha and C. Eswaran, “Automated detection of anesthetic depth levels using chaotic features with artificial neural networks,” Journal of Medical Systems, vol. 31, no. 6, pp. 445–452, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. X.-S. Zhang, R. J. Roy, and E. W. Jensen, “EEG complexity as a measure of depth of anesthesia for patients,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 12, pp. 1424–1433, 2001. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Hornero, D. Abásolo, J. Escudero, and C. Gómez, “Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease,” Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 367, no. 1887, pp. 317–336, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  15. R. G. Andrzejak, F. Mormann, G. Widman, T. Kreuz, C. E. Elger, and K. Lehnertz, “Improved spatial characterization of the epileptic brain by focusing on nonlinearity,” Epilepsy Research, vol. 69, no. 1, pp. 30–44, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, vol. 7, Cambridge University Press, 2004.
  17. A. Babloyantz and A. Destexhe, “Low-dimensional chaos in an instance of epilepsy,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 83, no. 10, pp. 3513–3517, 1986. View at Publisher · View at Google Scholar · View at Scopus
  18. G. W. Frank, T. Lookman, M. A. H. Nerenberg, C. Essex, J. Lemieux, and W. Blume, “Chaotic time series analyses of epileptic seizures,” Physica D: Nonlinear Phenomena, vol. 46, no. 3, pp. 427–438, 1990. View at Publisher · View at Google Scholar · View at Scopus
  19. L. D. Iasemidis, H. P. Zaveri, J. C. Sackellares, and W. J. Williams, “Phase space analysis of EEG in temporal lobe epilepsy,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1201–1203, November 1988. View at Scopus
  20. 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
  21. S. Micheloyannis, E. Papanikolaou, E. Bizas, C. J. Stam, and P. G. Simos, “Ongoing electroencephalographic signal study of simple arithmetic using linear and non-linear measures,” International Journal of Psychophysiology, vol. 44, no. 3, pp. 231–238, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. N. Talebi, A. M. Nasrabadi, and T. Curran, “Investigation of changes in EEG complexity during memory retrieval: The effect of midazolam,” Cognitive Neurodynamics, vol. 6, no. 6, pp. 537–546, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. V. Müller, W. Lutzenberger, H. Preißl, F. Pulvermüller, and N. Birbaumer, “Complexity of visual stimuli and non-linear EEG dynamics in humans,” Cognitive Brain Research, vol. 16, no. 1, pp. 104–110, 2003. View at Publisher · View at Google Scholar · View at Scopus
  24. C. J. Stam, “Nonlinear dynamical analysis of EEG and MEG: review of an emerging field,” Clinical Neurophysiology, vol. 116, no. 10, pp. 2266–2301, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. J. C. McBride, X. Zhao, N. B. Munro et al., “Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease,” Computer Methods and Programs in Biomedicine, vol. 114, no. 2, pp. 153–163, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. R. Faigle, R. Sutter, and P. W. Kaplan, “The electroencephalography of encephalopathy in patients with endocrine and metabolic disorders,” Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, vol. 30, no. 5, 2013. View at Google Scholar
  27. R. Pool, “Is it healthy to be chaotic?” Science, vol. 243, no. 4891, pp. 604–607, 1989. View at Publisher · View at Google Scholar · View at Scopus
  28. F. Takens, “Detecting strange attractors in turbulence,” Lecture Notes in Mathematics, vol. 898, no. 1, pp. 366–381, 1981. View at Google Scholar
  29. W. S. Pritchard and D. W. Duke, “Measuring chaos in the brain: A tutorial review of nonlinear dynamical eeg analysis,” International Journal of Neuroscience, vol. 67, no. 1-4, pp. 31–80, 1992. View at Publisher · View at Google Scholar · View at Scopus
  30. A. M. Fraser and H. L. Swinney, “Independent coordinates for strange attractors from mutual information,” Physical Review A: Atomic, Molecular and Optical Physics, vol. 33, no. 2, pp. 1134–1140, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  31. A. M. Fraser, “Information and entropy in strange attractors,” Institute of Electrical and Electronics Engineers Transactions on Information Theory, vol. 35, no. 2, pp. 245–262, 1989. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. M. B. Kennel, R. Brown, and H. D. I. Abarbanel, “Determining embedding dimension for phase-space reconstruction using a geometrical construction,” Physical Review A: Atomic, Molecular and Optical Physics, vol. 45, no. 6, pp. 3403–3411, 1992. View at Publisher · View at Google Scholar · View at Scopus
  33. P. Grassberger and I. Procaccia, “Characterization of strange attractors,” Physical Review Letters, vol. 50, no. 5, pp. 346–349, 1983. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  34. P. Grassberger and I. Procaccia, “Measuring the strangeness of strange attractors,” Physica D: Nonlinear Phenomena, vol. 9, no. 1-2, pp. 189–208, 1983. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  35. A. Wolf, J. B. Swift, and H. L. a. Swinney, “Determining Lyapunov exponents from a time series,” Physica D: Nonlinear Phenomena, vol. 16, no. 3, pp. 285–317, 1985. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  36. M. T. Rosenstein, J. J. Collins, and C. J. de Luca, “A practical method for calculating largest Lyapunov exponents from small data sets,” Physica D: Nonlinear Phenomena, vol. 65, no. 1-2, pp. 117–134, 1993. View at Publisher · View at Google Scholar · View at Scopus
  37. D. P. Subha, P. K. Joseph, R. Acharya, and C. M. Lim, “EEG signal analysis: a survey,” Journal of Medical Systems, vol. 34, no. 2, pp. 195–212, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. P. W. Kaplan and R. Sutter, “Affair with triphasic waves—their striking presence, mysterious significance, and cryptic origins: What are they?” Journal of Clinical Neurophysiology, vol. 32, no. 5, pp. 401–405, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. A. B. Janati, N. AlGhasab, and M. Umair, “Focal triphasic sharp waves and spikes in the electroencephalogram,” Neurological Sciences, vol. 36, no. 2, pp. 221–226, 2015. View at Publisher · View at Google Scholar · View at Scopus