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Seizure Prediction in Epilepsy

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Neural Engineering

Part of the book series: Bioelectric Engineering ((BEEG))

Abstract

Epilepsy is the second most common serious neurological disease after stroke. This disease affects approximately 50 million people worldwide and 50-70 cases per 100,000 in the developed countries. In approximately 40% of patients with so-called partial seizures, current medications are unable to control their symptoms. One of the most devastating aspects of epilepsy is the anxiety and apprehension associated with the inability to predict when a seizure will occur. The inability to predict the time of seizure onset also implies the need for continuous medication therapy with the associated continuous side effects. For a number of years investigators and commercial-interest groups have sought methods for the early detection and anticipation of seizures so that “discontinuous” therapies could be introduced (e.g., Milton and Jung, 2003). At the heart of most predictive efforts is the description and analysis of the cerebral electrical activity reflected in the electroencephalogram (EEG). The brain electrical activity of a patient with epilepsy shows abnormal and often rhythmic discharges during the seizure. This activity pattern is called an electrographic seizure. Between such electrographic seizures, short discharges (spikes) are also frequently observed in the EEG of these patients. Identification of these activity patterns in clinical practice has typically been a subjective process. The introduction of computer-based instrumentation and analysis to the field of electroencephalography made evaluation of automated spike and seizure detection techniques possible (e.g., Gotman and Gloor, 1976; Gotman et ai, 1979; Gotman, 1982). During the 1980s, the EEG during seizure activity was characterized using more complex measures such as those derived from chaos theory (e.g., Babloyantz and Destexhe, 1986; van Erp, 1988). There were a number of “early” reports of the successful application of frequency-domain template analyses and auto-regressive models to the problem of seizure prediction (e.g., Viglione and Walsh, 1975; Rogowski et al, 1981).

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References

  • American Electroencephalographic Society, 1994, American Electroencephalographic Society. Guideline thirteen: Guidelines for standard electrode position nomenclature, J. Clin. Neurophysiol. 11:111–113.

    Article  Google Scholar 

  • Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C. E., 2001a, Indications of nonlinear deterministic an infinite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E 64:061907-1–061907-7.

    Article  Google Scholar 

  • Andrzejak, R. G., Widman, G., Lehnertz, K., Rieke, C., David, P., and Elger, C. E., 2001b, The epileptic process as nonlinear deterministic dynamics in a stochastic environment: An evaluation on mesial temporal lobe epilepsy, Epilepsy Res. 44:129–140.

    Article  Google Scholar 

  • Babloyantz, A., and Destexhe, A., 1986, Low-dimensional chaos in an instance of epilepsy, Proc. Natl. Acad. Sci. USA 83:3513–3517.

    Article  Google Scholar 

  • Casdagli, M. C., Iasemidis, L. D., Sackellares, J. C., Roper, S. N., Gilmore, R. L., and Savit, R. S., 1996, Characterizing nonlinearity in invasive EEG recordings from temporal lobe epilepsy, Physica D 99:381–389.

    Article  MATH  Google Scholar 

  • Chavez, M., Le Van Quyen, M., Navarro, V., Baulac, M., and Martinerie, J., 2003, Spatio-temporal dynamics prior to neocortical seizures: Amplitude versus phase coupling, IEEE Trans. Biomed. Eng. 50:571–583.

    Article  Google Scholar 

  • Chkhenkeli, S. A., 2003, Direct deep-brain stimulation: First steps towards the feedback control of seizures, In: Epilepsy as a Dynamic Disease (J. Milton and P. Jung, eds.), Springer-Verlag, Berlin, pp. 249–261.

    Google Scholar 

  • Commission on Classification and Terminology of the International League Against Epilepsy, 1981, Proposal for revised clinical and electroencephalographic classification of epileptic seizures, Epilepsia 22:489–501.

    Article  Google Scholar 

  • Commission Report International League Against Epilepsy, 2001, Glossary of descriptive terminology for ictal semiology: Report of the ILAE task force on classification and terminology, Epilepsia 42:1212–1218.

    Article  Google Scholar 

  • D’Allessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J., and Litt, B., 2003, Epilepsy seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: A report of four patients, IEEE Trans. Biomed. Eng. 50:603–614.

    Article  Google Scholar 

  • Ebersole, J. S., 2001, Editorial: The last word, J. Clin. Neurophysiol. 18:299–300.

    Article  Google Scholar 

  • Elbert, T., Ray, W. J., Kowalik, Z. J., Skinner, J. E., Graf, K. E., and Birbaumer, N., 1994, Chaos and physiology: Deterministic chaos in excitable cell assemblies, Physiol. Rev. 74:1–47.

    Google Scholar 

  • Elger, C. E., and Lehnertz, K., 1998, Seizure prediction by non-linear time series analysis of brain electrical activity, Eur. J. Neurosci. 10:786–789.

    Article  Google Scholar 

  • Engel, J., 2001, A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: Report of the ILAE task force on classification and terminology, Epilepsia 42:796–803.

    Article  Google Scholar 

  • Feigenbaum, M. J., 1983, Universal behavior in nonlinear systems, Physica D 7:16–39.

    Article  MathSciNet  Google Scholar 

  • Ghorui, S., Das, A. K., and Venkatramani, N., 2000, A simpler and elegant algorithm for computing fractal dimension in higher dimensional state space, Pramana 54:L331–L336.

    Article  Google Scholar 

  • Gotman, J., 1982, Automatic recognition of epileptic seizures in the EEG, Electroencephalogr. Clin. Neurophysiol. 54:530–540.

    Article  Google Scholar 

  • Gotman, J., and Gloor, P., 1976, Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG, Electroencephalogr. Clin. Neurophysiol. 41:513–529.

    Article  Google Scholar 

  • Gotman, J., Ives, J. R., and Gloor, P., 1979, Automatic recognition of inter-ictal epileptic activity in prolonged EEG recordings, Electroencephalogr. Clin. Neurophysiol. 46:510–520.

    Article  Google Scholar 

  • Grassberger, P., and Procaccia, I., 1983a, Characterization of strange attractors, Phys. Rev. Lett. 50:346–349.

    Article  MathSciNet  Google Scholar 

  • Grassberger, P., and Procaccia, I., 1983b, Estimation of the Kolmogorov entropy from a chaotic signal, Phys. Rev. A 28:2591–2593.

    Article  Google Scholar 

  • Hively, L. M., Gailey, P. C., and Protopopescu, V. A., 1999, Detecting dynamical change in nonlinear time series, Phys. Lett. A 258:103–114.

    Article  Google Scholar 

  • Hively, L. M., and Protopopescu, V. A., 2003, Channel-consistent forewarning of epileptic events from scalp EEG, IEEE Trans. Biomed. Eng. 50:584–593.

    Article  Google Scholar 

  • Hodgkin, A. L., and Huxley, A. F., 1952, A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiol. (London) 117:500–544.

    Google Scholar 

  • Iasemidis, L. D., 2003, Epileptic seizure prediction and control, IEEE Trans. Biomed. Eng. 50:549–558.

    Article  Google Scholar 

  • Iasemidis, L. D., Sackellares, J. C., Zaveri, H. P., and Williams, W. J., 1990, Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures, Brain Topogr. 2:187–201.

    Article  Google Scholar 

  • Iasemidis, L. D., and Sackellares, J. C., 1991, The evolution with time of the spatial distribution of the largest Lyapunov exponent on the human epileptic cortex, In: Measuring Chaos in the Human Brain (D. Duke and W. Pritchard, eds.), World Scientific, Singapore, pp. 49–82.

    Google Scholar 

  • Iasemidis, L. D., Sackellares, J. C., and Savit, R. S., 1993, Quantification of hidden time dependencies in the EEG within the framework of nonlinear dynamics, In: Nonlinear Dynamical Analysis of the EEG (B. H. Jansen and M. E. Brand, eds.), World Scientific, Singapore, pp. 30–47.

    Google Scholar 

  • Iasemidis, L. D., Principe, J. C., Czaplewski, J. M., Gilmore, R. L., Roper, S. N., and Sackellares, J. C., 1997, Spatiotemporal transition to epileptic seizures: A nonlinear dynamical analysis of scalp and intracranial EEG recordings, In: Spatiotemporal Models in Biological and Artificial Systems (F. H. Lopes da Silva, J. C. Principe, and L. B. Almeida, eds.), IOS Press, Amsterdam, pp. 81–88.

    Google Scholar 

  • Iasemidis, L. D., Shiau, D.-S., Chaovalitwongse, W., Sackellares, J. C., Pardalos, P. M., Principe, J. C., Carney, P. R., Prasad, A., Veeramani, P., and Tsakalis, K., 2003, Adaptive epileptic seizure prediction system, IEEE Trans. Biomed. Eng. 50:616–627.

    Article  Google Scholar 

  • Jerger, K. K., Netoff, T. I., Francis, J. T., Sauer, T., Pecora, L., Weinstein, S. L., and Schiff, S. J., 2001, Early seizure detection, J. Clin. Neurophysiol. 18:259–268.

    Article  Google Scholar 

  • Kantz, H., and Schreiber, T., 1997, Nonlinear Time Series Analysis, Cambridge University Press, Cambridge, UK.

    MATH  Google Scholar 

  • Kaplan, D., and Glass, L, 1995, Understanding Nonlinear Dynamics, Springer-Verlag, New York.

    MATH  Google Scholar 

  • Lange, H. H., Lien, J. P., Engel, J., and Crandall, P. H., 1983, Temporo-spatial patterns of pre-ictal spike activity in human temporal lobe epilepsy, Electroencephalogr. Clin. Neurophysiol. 56:543–555.

    Article  Google Scholar 

  • Le Van Quyen, M., Adam, C., Baulac, M., Martinerie, J., and Varela, F. J., 1998, Nonlinear interdependencies of EEG signals in human intracranially recorded temporal lobe seizures, Brain Res. 792:24–40.

    Article  Google Scholar 

  • Le Van Quyen, M., Martinerie, J., Baulac, M., and Varela, F., 1999, Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings, NeuroReport 10:2149–2155.

    Article  Google Scholar 

  • Le Van Quyen, M., Adam, C., Martinerie, J., Baulac, M., Clemenceau, S., and Varela, F., 2000, Spatio-temporal characterizations of non-linear changes in intracranial activities prior to human temporal lobe seizures, Eur. J. Neurosci. 12:2124–2134.

    Article  Google Scholar 

  • Le Van Quyen, M., Martinerie, J., Navarro, V., Baulac, M., and Varela, F. J., 2001a, Characterizing neurodynamic changes before seizures, J. Clin. Neurophysiol. 18:191–208.

    Article  Google Scholar 

  • Le Van Quyen, M., Martinerie, J., Navarro, V., Boon, P., D’Have, M., Adam, C., Renault, B., Varela, F. J., and Baulac, M., 2001b, Anticipation of epileptic seizures from standard EEG recordings, Lancet 357:183–188.

    Article  Google Scholar 

  • Lehnertz, K., and Elger, C. E., 1995, Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss, Electroencephalogr. Clin. Neurophysiol. 95:108–117.

    Article  Google Scholar 

  • Lehnertz, K., and Elger, C. E., 1998, Can epileptic seizures be predicted? Evidence from nonlinear time series analyses of brain electrical activity, Phys. Rev. Lett. 80:5019–5023.

    Article  Google Scholar 

  • Lehnertz, K., Widman, G., Andrzejak, R., Arnhold, J., and Elger, C. E., 1999, Is it possible to anticipate seizure onset by non-linear analysis of intracerebral EEG in human partial epilepsies? Rev. Neurol. 155:454–456.

    Google Scholar 

  • Lehnertz, K., Andrzejak, R. G., Arnhold, J., Kreuz, T., Mormann, F., Rieke, C., Widman, G., and Elger, C. E., 2001, Nonlinear EEG analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation, and prevention, J. Clin. Neurophysiol. 18:209–222.

    Article  Google Scholar 

  • Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., Pennell, P., Epstein, C., Bakay, R., Dichter, M., and Vachtsevanos, G., 2001, Epileptic seizures may begin hours in advance of clinical onset: A report of five patients, Neuron 30:51–64.

    Article  Google Scholar 

  • Lopes da Silva, F. H., Blanes, W., Kalitzin, S. N., Parra, J., Suffczynski, P., and Velis, D. N., 2003, Dynamical diseases of brain systems: Different routes to epileptic seizures, IEEE Trans. Biomed. Eng. 50:540–548.

    Article  Google Scholar 

  • Lorenz, E. N., 1963, Deterministic non-periodic flow, J. Atmos. Sci. 20:130–141.

    Article  Google Scholar 

  • Lytton, W. W., and Thomas, E., 1993, Modeling of thalamocortical oscillations, In: Cerebral Cortex, Vol. 13 (P. Ulinski et al. eds.), Plenum Publishers, New York, pp. 479–509.

    Google Scholar 

  • Manuca, R., and Savit, R., 1996, Stationarity and nonstationarity in time series analysis, Physica. D 99:134–161.

    Article  MATH  MathSciNet  Google Scholar 

  • Manuca, R., Casdagli, M. C., and Savit, R. S., 1998, Nonstationarity in epileptic EEG and implications for neural dynamics, Math. Biosci. 147:1–22.

    Article  MATH  Google Scholar 

  • McSharry, P. E., Smith, L. A., and Tarassenko, L., 2003, Comparison of predictability of epileptic seizures by a linear and a nonlinear method, IEEE Trans. Biomed. Eng. 50:628–633.

    Article  Google Scholar 

  • Milton, J. G., Remillard, G. M., and Anfermann, F., 1987, Timing of seizure recurrence in adult epileptic patients: A statistical analysis, Epilepsia 28:471–478.

    Article  Google Scholar 

  • Milton, J., and Jung, P. (eds.), 2003, Epilepsy as a Dynamic Disease, Springer-Verlag, Berlin.

    MATH  Google Scholar 

  • Mormann, F., Lehnertz, K., David, P., and Elger, C. E., 2000, Mean phase coherence as a measure for phase synchronization and its application to EEG of epilepsy patients, Physica D 144:358–369.

    Article  MATH  Google Scholar 

  • Netoff, T. I., and Schiff, S. J., 2002, Decreased neural synchronization during experimental seizures, J. Neurosci. 22:7297–7307.

    Google Scholar 

  • Nunez, P. L., 1981, Electric Fields of the Brain: The Neurophysics of EEG, Oxford University Press, Oxford.

    Google Scholar 

  • Peitgen, H. O., Jurgens, H., and Saupe, D., 1992, Chaos and Fractals: New Frontiers in Science, Springer-Verlag, New York.

    Google Scholar 

  • Pijn, J. P., Neervan, J. V., Noest, A., and Lopes da Silva, F. H., 1991, Chaos or noise in EEG signals: Dependence on state and brain site, Electroencephalogr. Clin. Neurophysiol. 79:371–381.

    Article  Google Scholar 

  • Protopopescu, V. A., Hively, L. M., and Gailey, P. C., 2001, Epileptic event forewarning from scalp EEG, J. Clin. Neurophysiol. 18:223–245.

    Article  Google Scholar 

  • Rieke, C., Mormann, F., Andrzejak, R. G., Kreuz, T., David, P., Elger, C. E., and Lehnertz, K., 2003, Discerning nonstationarity from nonlinearity in seizure-free and preseizure EEG recordings from epilepsy patients, IEEE Trans. Biomed. Eng. 50:634–639.

    Article  Google Scholar 

  • Rogowski, Z., Gath, I., and Bental, E., 1981, On the prediction of epileptic seizures, Biol. Cybern. 42:9–15.

    Article  Google Scholar 

  • Savit, R., Li, D., Zhou, W., and Drury, I., 2001, Understanding dynamic state changes in temporal lobe epilepsy, J. Clin. Neurophysiol. 18:246–258.

    Article  Google Scholar 

  • Schouten, J. C., Takens, F., and van den Bleek, C. M., 1994a, Maximum-likelihood estimation of the entropy of an attractor, Phys. Rev. E 49:126–129.

    Article  MathSciNet  Google Scholar 

  • Schouten, J. C., Takens, F., and van den Bleek, C. M., 1994b, Estimation of the dimension of a noisy attractor, Phys. Rev. E 50:1851–1861.

    Article  MathSciNet  Google Scholar 

  • Stam, C. J., Pijn, J. P., and Pritchard, W. S., 1998, Reliable detection of nonlinearity in experimental time series with strong periodic components, Physica D 112:361–380.

    Article  MATH  MathSciNet  Google Scholar 

  • Takens, F., 1981, Detecting strange attractors in turbulence, In: Lecture Notes in Mathematics, Dynamical Systems and Turbulence, Vol. 898 (D. A. Rand and L. S. Young, eds.), Springer-Verlag, Berlin, pp. 366–381.

    Google Scholar 

  • Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., and Farmer, J. D., 1992, Testing for nonlinearity in time series: The method of surrogate data, Physica D 58:77–94.

    Article  Google Scholar 

  • Towle, V. L., Carder, R. K., Khorasani, L., and Lindberg, D., 1999, Electrographic coherence patterns, J. Clin. Neurophysiol. 16:528–547.

    Article  Google Scholar 

  • Traub, R. D., and Llinas, R., 1979, Hippocampal pyramidal cells: Significance of dendritic ionic conductances for neuronal function and epileptogenesis, J. Neurophysiol. 42:476–496.

    Google Scholar 

  • van Drongelen, W., Hereld, M., Lee, H. C., Papka, M. E., and Stevens, R. L., 2002, Simulation of neocortical activity, Epilepsia 43(Suppl. 7):149.

    Google Scholar 

  • van Drongelen, W., Nayak, S., Frim, D. M., Kohrman, M. H., Towle, V. L., Lee, H. C., McGee, A. B., Chico, M. S., and Hecox, K. E., 2003a, Seizure anticipation in pediatric epilepsy: Use of Kolmogorov entropy, Pediatr. Neurol. 29:207–213.

    Article  Google Scholar 

  • van Drongelen, W., Koch, H., Marcuccilli, C., Pena, F., and Ramirez, J. M., 2003b, Synchrony levels during evoked seizure-like bursts in mouse neocortical slices, J. Neurophysiol. 90:1571–1580.

    Article  Google Scholar 

  • van Erp, M. G., 1988, On Epilepsy: Investigations on the Level of the Nerve Membrane and of the Brain, Thesis, Leiden, The Netherlands.

    Google Scholar 

  • Viglione, S., and Walsh, G., 1975, Epileptic seizure prediction, Electroencephalogr. Clin. Neurophysiol. 39:435–436.

    Google Scholar 

  • Wolf, A., Swift, J. B., Swinney, H. L., and Vastano, J. A., 1985, Determining Lyapunov exponents from a time series, Physica D 16:285–317.

    Article  MATH  MathSciNet  Google Scholar 

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  1. Department of Pediatrics, The University of Chicago, MC 3055, 5841 S Maryland, Chicago, Ilinois, 60637

    Wim van Drongelen, Hyong C. Lee & Kurt E. Hecox

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  1. Wim van Drongelen
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  2. Hyong C. Lee
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  1. University of Minnesota, Minneapolis, Minnesota

    Bin He

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van Drongelen, W., Lee, H.C., Hecox, K.E. (2005). Seizure Prediction in Epilepsy. In: He, B. (eds) Neural Engineering. Bioelectric Engineering. Springer, Boston, MA. https://doi.org/10.1007/0-306-48610-5_12

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