Skip to main content
SpringerLink
Log in

Online Prediction of Onsets of Seizure-like Events in Hippocampal Neural Networks Using Wavelet Artificial Neural Networks

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

It has been previously shown that wavelet artificial neural networks (WANNs) are able to classify the different states of epileptiform activity and predict the onsets of seizure-like events (SLEs) by offline processing (Ann. Biomed. Eng. 33(6):798–810, 2005) of the electrical data from the in-vitro hippocampal slice model of recurrent spontaneous SLEs. The WANN design entailed the assumption that time-varying frequency information from the biological recordings can be used to estimate the times at which onsets of SLEs would most likely occur in the future. Progressions of different frequency components were captured by the artificial neural network (ANN) using selective frequency inputs from the initial wavelet transform of the biological data. The training of the WANN had been established using 184 SLE episodes in 34 slices from 21 rats offline. Nine of these rats also exhibited periods of interictal bursts (IBs). These IBs were included as part of the training to help distinguish the difference in dynamics of bursting activities between the preictal- and interictal type. In this paper, we present the results of an online processing using WANN on 23 in-vitro rat hippocampal slices from 9 rats having 93 spontaneous SLE episodes generated under low magnesium conditions. Over the test cases, three of the nine rats exhibited over 30 min of IB activities. We demonstrated that the WANN was able to classify the different states, namely, interictal, preictal, ictal, and IB activities with an accuracy of 86.6, 72.6, 84.5, and 69.1%, respectively. Prediction of state transitions into ictal events was achieved using regression of initial “normalized time-to-onset” estimates. The SLE onsets can be estimated up to 36.4 s ahead of their actual occurrences, with a mean error of 14.3 ± 27.0 s. The prediction errors decreased progressively as the actual time-to-onset decreased and more initial “normalized time-to-onset” estimates were used for the regression procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

FIGURE 1.
FIGURE 2.
FIGURE 3.
FIGURE 4.
FIGURE 5.
FIGURE 6.
FIGURE 7.
FIGURE 8.

Similar content being viewed by others

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.

    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.

    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. Basar, E. Brain Function and Oscillations, vol. 1. Berlin: Springer-Verlag, 1998.

  5. Bishop, C. M. Neural Networks for Pattern Recognition. Clarendon Press, 1995.

  6. Bragin, A., J. Engel, C. L. Wilson, I. Fried, and G. Buzs\(\acute{\rm a}\)ki. High-frequency oscillations in human brain. Hippocampus 9(2):137–142, 1999.

  7. Braunwald, E., A. S. Fauci, D. L. Kasper, S. L. Hauser, D. L. Longo, and J. L. Jameson, eds., 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.

    Google Scholar 

  9. Chiu, A. W. L., M. Cotic, S. S. Jahromi, H. Khosravani, P. L. Carlen, and B. L. Bardakjian. The effects of high frequency oscillations in hippocampal electrical activities on the classification of epileptiform events using artificial neural networks. J. Neural Eng. 3:9–20, 2006.

    Google Scholar 

  10. Chiu, A. W. L., S. Daniel, H. Khosravani, P. L. Carlen, and B. L. Bardakjian. Prediction of seizure onsets in an in-vitro hippocampal slice model of epilepsy using gaussian-based and wavelet-based artificial neural networks. Ann. Biomed. Eng. 33(6):798–810, 2005.

    Google Scholar 

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

    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.

    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.

    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.

    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.

    Google Scholar 

  16. Dzhala, V. I., and K. J. Staley. Mechanisms of fast ripples in the hippocampus. J. Neurosci. 24(40):8896–8906, 2004.

    Google Scholar 

  17. Engel, J., Jr., 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. Engel Jr. New York: Raven, 1993, pp. 609–622.

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

    Google Scholar 

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

    Google Scholar 

  20. Haykin, S. Neural Networks: A Comprehensive Foundation. Englewood Cliffs: Prentice-Hall, 1998.

  21. 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.

    Google Scholar 

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

    Google Scholar 

  23. Iasemidis, L. D., J. C. Principe, and J. C. Sackellares. Measurement and quantification of spatio-temporal dynamics of human epileptic seizures. In: Nonlinear Signal Processing in Medicine, edited by M. Akay. IEEE Press, 1999.

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

    Google Scholar 

  25. 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.

    Google Scholar 

  26. 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.

  27. 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.

    Google Scholar 

  28. 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.

    Google Scholar 

  29. 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.

    Google Scholar 

  30. 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.

    Google Scholar 

  31. 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.

    Google Scholar 

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

  33. 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.

  34. Orb\(\acute{a}\)n, G., T. Kiss, M. Lengyel, and P. \(\acute{E}\)rdi. Hippocampal rhythm generation: Gamma-related theta-frequency resonance in CA3 interneurons. Biol. Cybernet. 84:123–132, 2001.

  35. 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.

    Google Scholar 

  36. Perez-Velazquez, J. L., H. Khosravani, A. Lozano, B. L. Bardakjian, P. L. Carlen, and R. Wennberg. Type III intermittency in human partial epilepsy. Eur. J. Neurosci. 11:2571–2576, 1999.

    Google Scholar 

  37. 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.

    Google Scholar 

  38. 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.

    Google Scholar 

  39. 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 

  40. 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.

    Google Scholar 

  41. So, P., J. T. Francis, T. I. Netoff, B. J. Gluckman, and S. J. Schiff. Periodic orbits: A new language for neuronal dynamics. Biophys. J. 74:2776–2785, 1998.

    Google Scholar 

  42. 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.

    Google Scholar 

  43. Valiante, T., A. W. L. Chiu, and B. L. Bardakjian. Neural codes in human extracranial EEG: Identification of epilepsy features. In: International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China, 2005.

  44. 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.

    Google Scholar 

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

    Google Scholar 

  46. 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.

    Google Scholar 

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

Download references

ACKNOWLEDGMENTS

This work has been supported by the Natural Sciences and Engineering Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR) and the Krembil Scientist Development Seed Fund.

Author information

Authors and Affiliations

  1. Institute of Biomaterials and Biomedical Engineering, Toronto, Canada

    Alan W. L. Chiu, Peter L. Carlen &  Berj L. Bardakjian

  2. Department of Electrical and Computer Engineering, Toronto, Canada

    Alan W. L. Chiu, Peter L. Carlen &  Berj L. Bardakjian

  3. Department of Physiology, Toronto, Canada

    Eunji E. Kang, Miron Derchansky & Peter L. Carlen

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

    Eunji E. Kang, Miron Derchansky & Peter L. Carlen

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

    Berj L. Bardakjian

Authors
  1. Alan W. L. Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eunji E. Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miron Derchansky
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter L. Carlen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Berj L. Bardakjian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Berj L. Bardakjian.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chiu, A.W.L., Kang, E.E., Derchansky, M. et al. Online Prediction of Onsets of Seizure-like Events in Hippocampal Neural Networks Using Wavelet Artificial Neural Networks. Ann Biomed Eng 34, 282–294 (2006). https://doi.org/10.1007/s10439-005-9029-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-005-9029-9

Keywords

Search

Navigation