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Extraction and Analysis of Early Ictal Activity in Subdural Electroencephalogram

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Abstract

Subdural electroencephalograms (SEEGs) are of great value in localizing primary epileptogenic regions in patients undergoing evaluation for focal resective epilepsy surgery. The data segments which contain a transition from interictal to ictal activity carry the most critical diagnostic information. Computer signal extraction within this transition period represents a challenging signal processing problem. In this work a two-step method is presented to extract early ictal activity. In the first step we employ a nonlinear signal decomposition technique in the wavelet domain to separate SEEG data into ictal and background components. In the second step we use time-frequency analysis and a novel integration algorithm to extract the desired information. Our experiments on clinically recorded data indicate that this method is highly effective allowing us to reveal important hidden features in the data which could not otherwise be observable. © 2001 Biomedical Engineering Society.

PAC01: 8719Nn, 0260Jh

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REFERENCES

  1. Burrus, C. S., R. A. Gopinath, and H. Gau. Introduction to Wavelets and Wavelet Transforms, A Primer. Englewood Cliffs, NJ: Prentice-Hall, 1997.

    Google Scholar 

  2. Casdagli, M. C., L. D. Iasemidis., R. S. Savit., R. L. Gilmore., S. N. Roper., and J. C. Sackellares. Nonlinearity in invasive EEG recordings from patients with temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol.102.:98–105., 1997.

    Google Scholar 

  3. Chui, C. K. An Introduction to Wavelets. Boston, MA: Academic, 1992.

    Google Scholar 

  4. Cohen, A., I. Daubechies., and J. C. Feauveau. Biorthogonal bases of compactly supported wavelets. Commun. Pure Appl. Math.45.:485–560., 1992.

    Google Scholar 

  5. Cohen, L. Time-Frequency Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1995.

    Google Scholar 

  6. Daubechies, I..Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math.41.:909–996., 1992.

    Google Scholar 

  7. Donoho, D. L., and I. M. Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika.81.:425–455., 1994.

    Google Scholar 

  8. Donoho, D. L., I. M. Johnstone., G. Kerkyacharian., and D. Picard. Wavelet shrinkage: asymptopia?.J. Royal Stat. Soc. B.57.:301–365., 1995.

    Google Scholar 

  9. Hlawatsch, F., and G. F. Boudreaux-Bartels. Linear and quadratic time-frequency signal representation. IEEE Signal Process. Mag. 21–67, 1992.

  10. Iasemidis, L. D., K. E. Pappas, J. C. Principe, and J. C. Sackellares. Spatiotemporal dynamics of human epileptic seizures. In: Proc. 3rd Experimental Chaos Conference, Singapore: World Scientific, 1996, pp. 26–30.

  11. Iasemidis, L. D., J. C. Principe, J. M. Czaplewski, R. L. Gilmore, S. N. Roper, and J. C. Sackellares: Spatiotemporal transition to epileptic seizures: A nonlinear dynamical analysis of scalp and intracranial EEG recordings. In: Spatiotemporal Models in Biological Artificial Systems, edited by Silva et al. Washington DC: IOS, 1997, pp. 81–88.

    Google Scholar 

  12. Wieser, H. G..Epilepsy surgery: Past, present and future. Seizure.7.:173–184., 1998.

    Google Scholar 

  13. Oppenheim, A. V., and R. W. Schafer. Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.

    Google Scholar 

  14. Rabiner, L. R., and B. Gold. Theory and Application of Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1975.

    Google Scholar 

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

    Google Scholar 

  16. Mallat, S. G. A Wavelet Tour of Signal Processing. Boston, MA: Academic, 1998.

    Google Scholar 

  17. Schiff, S. J., P. So., and T. Chang. Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. Phys. Rev.54.:6708–6724., 1996.

    Google Scholar 

  18. Stam, C. J., J. Nicolai., and R. W. Keunen. Nonlinear dynamical analysis of periodic lateralized epileptiform discharges. Clin Electroencephalogr.29.:101–105., 1998.

    Google Scholar 

  19. Sun, M., C-C. Li., L. N. Sekhar., and R. J. Sclabassi. Efficient computation of discrete pseudo-Wigner distribution. IEEE Trans. Acoust., Speech, Signal Process.37.:1135–1142., 1989.

    Google Scholar 

  20. Yaylali, I., H. Kocak., and P. Jayakar. Detection of seizures from small samples using nonlinear dynamic system theory. IEEE Trans. Biomed. Eng.43.:743–751., 1996.

    Google Scholar 

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Authors and Affiliations

  1. Laboratory for Computational Neuroscience, Department of Neurosurgery, Department of Neurology, Department of Electrical Engineering, and Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA

    Mingui Sun, Mark L. Scheuer & Robert J. Sclabassi

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  1. Mingui Sun
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  2. Mark L. Scheuer
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  3. Robert J. Sclabassi
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Sun, M., Scheuer, M.L. & Sclabassi, R.J. Extraction and Analysis of Early Ictal Activity in Subdural Electroencephalogram. Annals of Biomedical Engineering 29, 878–886 (2001). https://doi.org/10.1114/1.1408928

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