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Prediction of Seizure Onset in an In-Vitro Hippocampal Slice Model of Epilepsy Using Gaussian-Based and Wavelet-Based Artificial Neural Networks

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Abstract

We propose that artificial neural networks (ANNs) can be used to predict seizure onsets in an in-vitro hippocampal slice model capable of generating spontaneous seizure-like events (SLEs) in their extracellular field recordings. This paper assesses the effectiveness of two ANN prediction schemes: Gaussian-based artificial neural network (GANN) and wavelet-based artificial neural network (WANN). The GANN prediction system consists of a recurrent network having Gaussian radial basis function (RBF) nonlinearities capable of extracting the estimated manifold of the system. It is able to classify the underlying dynamics of spontaneous in-vitro activities into interictal, preictal and ictal modes. It is also able to successfully predict the onsets of SLEs as early as 60 s before. Improvements can be made to the overall seizure predictor design by incorporating time-varying frequency information. Consequently, the idea of WANN is considered. The WANN design entails the assumption that frequency variations in the extracellular field recordings can be used to compute the times at which onsets of SLEs are most likely to occur in the future. Progressions of different frequency components can be captured by the ANN using appropriate frequency band adjustments via pruning, after the initial wavelet transforms. In the off-line processing comprised of 102 spontaneous SLEs generated from 14 in-vitro rat hippocampal slices, with half of them used for training and the other half for testing, the WANN is able to predict the forecoming ictal onsets as early as 2 min prior to SLEs with over 75% accuracy within a 30 s precision window.

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References

  1. Andrzejak, R. G., K. Lehnertz, F. Mormann, C. Rieke, and P. David. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64:61907, 2001.

    Article  CAS  Google Scholar 

  2. Andrzejak, R. G., F. Mormann, T. Kreuz, C. Rieke, A. Kraskov, C. E. Elger, and K. Lehnertz. Testing the null hypothesis of the nonexistence of a preseizure state. Phys. Rev. E 67(1):010901(R), 2003.

    Article  Google Scholar 

  3. Babloyantz, A., and A. Destexhe. Low-dimensional chaos in an instance of epilepsy. Proc. Natl. Acad. Sci. U.S.A. 83:3515–3517, 1986.

    Google Scholar 

  4. Bardakjian, B. L., and N. E. Diamant. A mapped clock oscillator model for transmembrane electrical rhythmic activity in excitable cells. J. Theor. Biol. 166:225–235, 1994.

    Article  CAS  PubMed  Google Scholar 

  5. Basar, E. Brain Function and Oscillations, Vol. 1. Berlin: Springer Verlag, 1998.

    Google Scholar 

  6. Blanco, S., A. Figliola, R. Q. Quiroga, O. A. Rosso, and E. Serrano. Time–frequency analysis of electroencephalogram series. III: Wavelet packets and information cost function. Phys. Rev. E 57(1):932–940, 1998.

    Article  CAS  Google Scholar 

  7. Braunwald, E., A. S. Fauci, D. L. Kasper, S. L. Hauser, D. L. Longo, and J. L. Jameson, editors. Harrison’s Principles of Internal Medicine. McGraw-Hill Professional, 2001.

  8. Chiu, A. W. L., and B. L. Bardakjian. Control of state transitions in an in-silico model of epilepsy using small perturbations. IEEE Trans. Biomed. Eng. 51(10):1856–1859, 2004.

    Article  PubMed  Google Scholar 

  9. Courville, A., and B. L. Bardakjian. Chaosmakers: Rhythmicity breakers. IEEE First Joint BMES/EMBS Conf. 1:406, 1999.

    Article  Google Scholar 

  10. Cowper, M. R., B. Mulgrew, and C. P. Unsworth. Nonlinear prediction of chaotic signals using a normalised radial basis function network. Signal Process. 82:775–789, 2002.

    Article  Google Scholar 

  11. D’Alessandro, M., R. Esteller, G. Vachtsevanos, A. Hinson, J. Echauz, and B. Litt. Epileptic seizure prediction using hybrid feature sselection over multiple intracranial {EEG} electrode contacts: A report of four patients. IEEE Trans. Biomed. Eng. 20(5):603–615, 2003.

    Article  Google Scholar 

  12. Draguhn, A., R. D. Traub, D. Schmitz, and J. G. R. Jefferys. Electrical coupling underlies high-frequency oscillations in the hippocampus in vitro. Nature 394:189–192, 1998.

    Article  CAS  PubMed  Google Scholar 

  13. Dreier, J. P., and U. Heinemann. Regional and time dependent variations of low Mg2+ induced epileptiform activity in rat temporal cortex slices. Exp. Brain Res. 87(3):581–596, 1991.

    Article  CAS  PubMed  Google Scholar 

  14. Durand, D. M., and M. Bikson. Suppression and control of epileptiform activity by electrical stimulation: A review. Proc. IEEE 89:1065–1082, 2001.

    Article  Google Scholar 

  15. Dzhala, V. I., and K. J. Staley. Transition from interictal to ictal activity in limbic networks in vitro. J. Neurosci. 23:7873–7880, 2003.

    CAS  PubMed  Google Scholar 

  16. Dzhala, V. I., and K. J. Staley. Mechanisms of fast ripples in the hippocampus. J. Neurosci. 24:8896–8906, 2004.

    Article  CAS  PubMed  Google Scholar 

  17. Finkel, L. H. Neuroengineering models of brain disease. Annu. Rev. Biomed. Eng. 2:577–606, 2000.

    Article  CAS  PubMed  Google Scholar 

  18. Gluckman, B., H. Nguyen, S. Weinstein, and S. Schiff. Adaptive electric field control of epileptic seizures. J. Neurosci. 21(2):590–600, 2001.

    CAS  PubMed  Google Scholar 

  19. Haykin, S. Neural networks: A comprehensive foundation. New Jersey: Prentice-Hall, 1998.

    Google Scholar 

  20. Haykin, S., R. Racine, Y. Xu, and C. Chapman. Monitoring neuronal oscillations and signal transmission between cortical regions using time–frequency analysis of electroencephalographic activity. Proc. IEEE 84(9):1295–1301, 1996.

    Article  Google Scholar 

  21. Iasemidis, L. D. Epileptic seizure prediction and control. IEEE Trans. Biomed. Eng. 20(5):549–558, 2003.

    Article  Google Scholar 

  22. Iasemidis, L. D., and J. C. Sackellares. Chaos theory and epilepsy. Neuroscientist 2:118–126, 1996.

    Google Scholar 

  23. Iasemidis, L. D., 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 systems. IEEE Trans. Biomed. Eng. 50(5):616–627, 2003.

    Article  PubMed  Google Scholar 

  24. Jerger, K. K., T. I. Netoff, J. T. Francis, T. Sauer, L. Pecora, S. L. Weinstein, and S. J. Schiff. Comparison of methods for seizure detection. In: Epilepsy as a Dynamic Diseases, edited by J. Milton and P. Jung. Berlin: Springer-Verlag, 2003, pp. 237–248.

  25. Jr., J. E., P. C. V. Ness, T. B. Rasmussen, and L. M. Ojemann. Outcome with respect to epileptic seizures. In: Surgical Treatment of the Epilepsies, edited by J. E. Jr. New York: Raven, 1993, pp. 609–622.

  26. Kalitzin, S. N., J. Parra, D. N. Velis, and F. H. L. da Silva. Enhancement of phase clustering in the {EEG/MEG} gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with known visual sensitivity. IEEE Trans. Biomed. Eng. 49:1279–1286, 2002.

    Article  PubMed  Google Scholar 

  27. Khosravani, H., P. L. Carlen, and J. L. Perez-Velazquez. The control of seizure-like activity in rat hippocampal slice. Biophys. J. 84:687–695, 2003.

    CAS  PubMed  Google Scholar 

  28. Lasztoczi, B., K. Antal, L. Nyikos, Z. Emri, and J. Kardos. High-frequency synaptic input contributes to seizure initiation in the low Mg2+ model of epilepsy. Eur. J. Neurosci. 19:1361–1372, 2004.

    Article  PubMed  Google Scholar 

  29. Lehnertz, K., G. Widman, and C. Elger. Can epileptic seizures be predicted? Evidence from nonlinear time series of brain electrical activity. Phys. Rev. Lett. 80:5019–5022, 1998.

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  31. Mallat, S. A Wavelet Tour of Signal Processing. San Diego: Academic Press, 1998.

    Google Scholar 

  32. Milton, J., and P. Jung. Brain defibrillators: Synopsis, problems and future directions. In: Epilepsy as a Dynamic Diseases, edited by J. Milton and P. Jung. Berlin: Springer-Verlag, 2003, pp. 341–354.

  33. Orbán, G., T. Kiss, M. Lengyel, and P. Érdi. Hippocampal rhythm generation: Gamma-related theta-frequency resonance in {CA3} interneurons. Biol. Cybernet. 84:123–132, 2001.

    Article  Google Scholar 

  34. Osorio, I., M. G. Frei, and S. B. Wilkinson. Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsia 39(6):615–627, 1998.

    CAS  PubMed  Google Scholar 

  35. Rafiq, A., R. J. DeLorenzo, and D. A. Coulter. Generation and propagation of epiletiform discharges in a combined entorhinal cortex/hippocampal slice. J. Neurophysiol. 70(5):1962–1974, 1993.

    CAS  PubMed  Google Scholar 

  36. Rafiq, A., Y. Zhang, R. J. Delorenzo, and D. A. Coulter. Long-duration self-sustained epileptiform activity in the hippocampal–parahippocampal slice: A model of status epilepticus. J. Neurophysiol. 74(5):2028–2042, 1995.

    CAS  PubMed  Google Scholar 

  37. Rosso, O. A., S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schurmann, and E. Basar. Wavelet entropy: A new tool for analysis of short durationbrain electricla signals. J. Neurosci. Methods 105:65–075, 2001.

    Article  CAS  PubMed  Google Scholar 

  38. Schiff, S. J., K. Jerger, D. H. Duong, T. Chang, M. L. Spano, and W. L. Ditto. Controlling chaos in the brain. Nature 370:615–620, 1994.

    Google Scholar 

  39. Slutzky, M. W., P. Cvitanovic, and D. J. Mogul. Manipulating epileptiform bursting in the rat hippocampus using chaos control and adaptive techniques. IEEE Trans. Biomed. Eng. 50(5):559–570, 2003.

    Article  PubMed  Google Scholar 

  40. Sun, M., M. L. Scheuer, and R. J. Sclabassi. Extraction and analysis of early ictal activity in subdural electroencephalogram. Ann. Biomed. Eng. 29(10):878–886, 2001.

    Article  CAS  PubMed  Google Scholar 

  41. Widman, G., D. Bingmann, K. Lehnertz, and C. E. Elger. Reduced signal complexity of intracellular recordings: A precursor for epileptiform activity? Brain Res. 836:156–163, 1999.

    Article  CAS  PubMed  Google Scholar 

  42. Worrell, G. A., L. Parish, S. D. Cranstoun, R. Jonas, G. Baltuch, and B. Litt. High-frequency oscillations and seizure generation in neocortical epilepsy. Brain 127:1496–1506, 2004.

    Article  PubMed  Google Scholar 

  43. Young, R. K. Wavelet Theory and Its Applications. Drodrecht: Kluwer Academic Publishers, 1993.

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Institute of Biomaterials and Biomedical Engineering, Edward S. Rogers Sr., Canada

    Alan W. L. Chiu, Sarit Daniel & Berj L. Bardakjian

  2. Department of Pharmacology and Therapeutics, University of Calgary, Calgary, Canada

    Houman Khosravani

  3. Toronto Western Research Institute, University of Toronto, Toronto, Canada

    Peter L. Carlen

  4. Institute of Biomaterials and Biomedical Engineering, University of Toronto, 4 Taddle Creek Road, Toronto, Ontario, Canada, M5S 3G9

    Berj L. Bardakjian

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  1. Alan W. L. Chiu
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  2. Sarit Daniel
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  4. Peter L. Carlen
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  5. Berj L. Bardakjian
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Correspondence to Berj L. Bardakjian.

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Chiu, A.W.L., Daniel, S., Khosravani, H. et al. Prediction of Seizure Onset in an In-Vitro Hippocampal Slice Model of Epilepsy Using Gaussian-Based and Wavelet-Based Artificial Neural Networks. Ann Biomed Eng 33, 798–810 (2005). https://doi.org/10.1007/s10439-005-2346-1

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  • DOI: https://doi.org/10.1007/s10439-005-2346-1

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