Cybernetics and Systems Analysis

, Volume 42, Issue 4, pp 483–495 | Cite as

A feedback control systems view of epileptic seizures

  • K. Tsakalis
  • N. Chakravarthy
  • Sh. Sabesan
  • L. D. Iasemidis
  • P. M. Pardalos


To understand basic functional mechanisms that cause epileptic seizures, the paper discusses some key features of theoretical brain functioning models. The hypothesis is put forward that a plausible reason for seizures is pathological feedback in brain circuitry. The analysis of such circuitry has an interesting physical interpretation and may be used to cure epilepsy.


feedback control system epileptic seizure coupled-oscillator model 


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  1. 1.
    P. Kwan and M. J. Brodie, “Early identification of refractory epilepsy,” New Engl. J. Medicine, 342, 314–319 (2000).CrossRefGoogle Scholar
  2. 2.
    J. F. Kerrigan, B. Litt, R. S. Fisher, S. Cranstoun, J. A. French, D. E. Blum, M. Dicher, A. Shetter, G. Baltuch, J. Jaggi, S. Krone, M. A. Brodie, M. Rise, and N. Graves, “Electrical stimulation of the anterior nucleus of the thalamus for the treatment of intractable epilepsy,” Epilepsia, 45(4), 346–354 (2004).CrossRefGoogle Scholar
  3. 3.
    E. H. Kossoff, E. K. Ritzl, J. M. Politsky, A. M. Murro, J. R. Smith, R. B. Duckrow, D. D. Spencer, and G. K. Bergey, “Effect of an external responsive neurostimulator on seizures and electrographic discharges during subdural electrode monitoring,” Epilepsia, 45(12), 1560–1567 (2004).CrossRefGoogle Scholar
  4. 4.
    I. Osorio, M.G. Frei, S. Sunderam, J. Giftakis, N. C. Bhavaraju, S. F. Schaffner, and S. B. Wilkinson, “Automated seizure abatement in humans using electrical stimulation,” Annals of Neurology, 57(2), 258–268 (2005).CrossRefGoogle Scholar
  5. 5.
    J. G. Milton, J. Gotman, G. M. Remillard, and F. Andermann, “Timing of seizure recurrence in adult epileptic patients: a statistical analysis,” Epilepsia, 28, 471–478 (1987).Google Scholar
  6. 6.
    W. Penfield, “The evidence for a cerebral vascular mechanism in epilepsy,” Ann. Int. Med., 7, 303–310 (1933).Google Scholar
  7. 7.
    W. G. Lennox, Science and Seizures, Harper, New York (1946).Google Scholar
  8. 8.
    L. D. Iasemidis, H. P. Zaveri, J. C. Sackellares, W. J. Williams, and T. W. Hood, “Nonlinear dynamics of electrocorticographic data,” J. Clinical Neurophysiology, No. 5 (1988).Google Scholar
  9. 9.
    L. D. Iasemidis and J. C. Sackellares, “The temporal evolution of the largest Lyapunov exponent on the human epileptic cortex,” in: D. W. Duke and W. S. Pritchard (eds.), Measuring Chaos in the Human Brain, World Scientific, Singapore (1991), pp. 49–82.Google Scholar
  10. 10.
    L. D. Iasemidis, D. S. Shiau, W. Chaovalitwongse, J. C. Sackellares, P. M. Pardalos, J. C. Principe, P. R. Carney, A. Prasad, B. Veeramani, and K. Tsakalis, “Adaptive epileptic seizure prediction system,” IEEE Trans. Biomedical Engineering, 50, 616–627 (2003).CrossRefGoogle Scholar
  11. 11.
    L. D. Iasemidis, D. S. Shiau, J. C. Sackellares, P. M. Pardalos, and A. Prasad, “Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques,” IEEE Trans. Biomedical Engineering, 51, 493–506 (2004).CrossRefGoogle Scholar
  12. 12.
    K. Lehnertz, G. Widman, and C. E. Elger, “Predicting seizures of mesial temporal and neocortical origin,” Epilepsia, 39 (1998).Google Scholar
  13. 13.
    K. Lehnertz, R. Andrzejak, J. Arnhold, T. Kreuz, F. Morman, C. Rieke, G. Widman, and C. E. Elger, “Nonlinear EEG analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation and prevention,” J. Clinical Neurophysiology, 18, 209–222 (2001).CrossRefGoogle Scholar
  14. 14.
    H. Stoegbauer, L. Yang, P. Grassberger, R. G. Andrzejak, T. Kreuz, A. Kraskov, C. E. Elger, and K. Lehnertz, “Lateralization of the focal hemisphere in mesial temporal lobe epilepsy using independent component analysis,” Epilepsia, 43 (2002).Google Scholar
  15. 15.
    M. Le Van Quyen, C. Adam, M. Baulac, J. M. Martinerie, and F. J. Varela, “Nonlinear interdependencies of EEG signals in human intracranially recorded temporal lobe seizures,” Brain Research, 792, 24–40 (1998).CrossRefGoogle Scholar
  16. 16.
    M. Le Van Quyen, J. M. Martinerie, V. Navarro, M. Baulac, and F. J. Varela, “Characterizing neurodynamic changes before seizures,” J. Clinical Neurophysiology, 18, 191–208 (2001).CrossRefGoogle Scholar
  17. 17.
    L. B. Good, S. Sabesan, L. D. Iasemidis, and D. M. Treiman, “Real-time control of epileptic seizures,” Proc. 3rd European Medical and Biological Engineering Conf., Prague, Czech Republic (2005).Google Scholar
  18. 18.
    L. D. Iasemidis, A. Prasad, J. C. Sackellares, P. M. Pardalos, and D. S. Shiau, “On the prediction of seizures, hysteresis and resetting of the epileptic brain: insights from models of coupled chaotic oscillators,” in: T. Bountis and S. Pneumatikos (eds.), Order and Chaos, Publ. House of K. Sfakianakis, Thessaloniki, Greece, p. 283–305, Proc. 14th Summer School on Nonlinear Dynamics: Chaos and Complexity, Patras, Gfreece (2001).Google Scholar
  19. 19.
    A. Skarda and W. J. Freeman, “How brains make chaos in order to make sense of the world?” Behav. Brain Sci., 10, 161–195 (1987).CrossRefGoogle Scholar
  20. 20.
    B. D. O. Anderson, “Adaptive systems, lack of persistency of excitation and bursting phenomenon,” Automatica, 21, 247–258 (1985).MATHCrossRefGoogle Scholar
  21. 21.
    W. A. Sethares, Jr., C. R. Johnson, and C. E. Rohrs, “Bursting in adaptive hybrids,” IEEE Trans. Commun., C-35, 791–799 (1989).CrossRefGoogle Scholar
  22. 22.
    K. S. Tsakalis, “Performance limitations of adaptive parameter estimation and system identification algorithms in the absence of excitation,” Automatica, 32, No. 4, 549–560 (1996).MATHMathSciNetCrossRefGoogle Scholar
  23. 23.
    K. S. Tsakalis, N. Chakravarthy, and L. D. Iasemidis, “Control of epileptic seizures: Model of chaotic oscillator networks,” Proc. IEEE CDC2005, Seville, Spain (2005).Google Scholar
  24. 24.
    R. D. Traub and A. Bibbig, “A model of high-frequency ripples in the hippocampus, based on synaptic coupling plus axon-axon gap junctions between pyramidal neurons,” J. Neurosci., 20, 2086–2093 (2000).Google Scholar
  25. 25.
    F. L. Da Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski, and D. N. Velis, “Epilepsies as dynamical diseases of brain systems: basic models of the transition between normal and epileptic activity,” Epilepsia, 44(Suppl. 12), 72–83 (2003).CrossRefGoogle Scholar
  26. 26.
    J. M. Carlson and J. Doyle, “Highly optimized tolerance: a mechanism for power laws in designed systems,” Physical Review E., 60, No. 2, 1412–1427 (1999).CrossRefGoogle Scholar
  27. 27.
    J. Doyle and J. M. Carlson, “Power laws, highly optimized tolerance, and generalized source coding,” Physical Review Letters, 84, No. 24, 5656–5659 (2000).CrossRefGoogle Scholar
  28. 28.
    K. Tsakalis, “Prediction and control of epileptic seizures,” in: Proc. Intern. Conf. and Summer School Complexity in Sci. and Society European Advanced Studies Conference V, July 14–26, Patras and Ancient Olympia, Greece (2004).Google Scholar
  29. 29.
    K. J. Astrom and L. Rundqwist, “Integrator windup and how to avoid it,” Proc. American Control Conf., Pittsburgh (1989), pp. 1693–1968.Google Scholar
  30. 30.
    E. Grassi, K. Tsakalis, S. Dash, S. V. Gaikwad, W. MacArthur, and G. Stein, “Integrated identification and PID controller tuning by frequency loop-shaping,” IEEE Trans. Contr. Systems Technology, 9, No. 2, 285–294 (2001).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • K. Tsakalis
    • 1
  • N. Chakravarthy
    • 1
  • Sh. Sabesan
    • 1
  • L. D. Iasemidis
    • 2
  • P. M. Pardalos
    • 3
  1. 1.Department of Electrical EngineeringArizona State UniversityUSA
  2. 2.Harrington Department of BioengineeringArizona State UniversityUSA
  3. 3.Department of Industrial and Systems Engineering, Department of Biomedical Engineering and McKnight Brain InstituteUniversity of FloridaUSA

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