Annals of Biomedical Engineering

, Volume 38, Issue 9, pp 3010–3021 | Cite as

A Nonlinear Model of Newborn EEG with Nonstationary Inputs

  • N. J. Stevenson
  • M. Mesbah
  • G. B. Boylan
  • P. B. Colditz
  • B. Boashash


Newborn EEG is a complex multiple channel signal that displays nonstationary and nonlinear characteristics. Recent studies have focussed on characterizing the manifestation of seizure on the EEG for the purpose of automated seizure detection. This paper describes a novel model of newborn EEG that can be used to improve seizure detection algorithms. The new model is based on a nonlinear dynamic system; the Duffing oscillator. The Duffing oscillator is driven by a nonstationary impulse train to simulate newborn EEG seizure and white Gaussian noise to simulate newborn EEG background. The use of a nonlinear dynamic system reduces the number of parameters required in the model and produces more realistic, life-like EEG compared with existing models. This model was shown to account for 54% of the linear variation in the time domain, for seizure, and 85% of the linear variation in the frequency domain, for background. This constitutes an improvement in combined performance of 6%, with a reduction from 48 to 4 model parameters, compared to an optimized implementation of the best performing existing model.


Newborn Neonate EEG Modelling and simulation Nonlinear Duffing oscillator Nonstationary Seizure 


  1. 1.
    Aarabi A., R. Grebe, and F. Wallois. A multistage knowledge-based system for EEG seizure detection in newborn infants. Clin. Neurophysiol. 118:2781–2797, 2007.CrossRefPubMedGoogle Scholar
  2. 2.
    Aminoff, M. J. Electrodiagnosis in Clinical Neurology. New York: Churchill Livingstone, 1992.Google Scholar
  3. 3.
    Boashash, B., E. J. Powers, and A. M. Zoubir. Higher Order Statistical Signal Processing. Melbourne: Longman Australia, 1995.Google Scholar
  4. 4.
    Budgor, A. B., K. Lindenberg, and K. E. Shuler. Studies in nonlinear stochastic processes. II. The Duffing oscillator revisited. J. Stat. Phys. 15:375–391, 1976.CrossRefGoogle Scholar
  5. 5.
    Celka, P., B. Boashash, and P. Colditz. Preprocessing and time–frequency analysis of newborn EEG seizures. IEEE Eng. Med. Biol. 20:30–39, 2001.CrossRefGoogle Scholar
  6. 6.
    Celka, P., and P. Colditz. Nonlinear nonstationary Wiener model of infant EEG seizures. IEEE Trans. Biomed. Eng. 49:556–564, 2002.CrossRefGoogle Scholar
  7. 7.
    Celka, P., and P. Colditz. A computer-aided detection of EEG seizures in infants: a singular spectrum approach and performance comparison. IEEE Trans. Biomed. Eng. 49:455–462, 2002.CrossRefGoogle Scholar
  8. 8.
    Conover, W. J. Practical Nonparametric Statistics, 3rd edn. New York: Wiley, 1999.Google Scholar
  9. 9.
    Deburchgraeve, W., P. J. Cherian, M. De Vos, R. M. Swarte, J. H. Blok, G. H. Visser, P. Govaert, and S. Van Huffel. Automated neonatal seizure detection mimicking a human observer reading EEG. Clin. Neurophysiol. 119:2447–2454, 2008.CrossRefPubMedGoogle Scholar
  10. 10.
    Deburchgraeve, W., P. J. Cherian, M. De Vos, R. M. Swarte, J. H. Blok, G. H. Visser, P. Govaert, and S. Van Huffel. Neonatal seizure localization using PARAFAC decomposition. Clin. Neurophysiol. 120:1787–1796, 2009.CrossRefPubMedGoogle Scholar
  11. 11.
    Faul, S., G. Gregorčič, G. Boylan, W. Marnane, G. Lightbody, and S. Connolly. Gaussian process modelling of EEG for the detection of neonatal seizures. IEEE Trans. Biomed. Eng. 54:2151–2162, 2007.CrossRefPubMedGoogle Scholar
  12. 12.
    Greene, B. R., S. Faul, W. P. Marnane, G. Lightbody, I. Korotchikova, and G. B. Boylan. A comparison of quantitative EEG features for neonatal seizure detection. Clin. Neurophysiol. 119:1248–1261, 2008CrossRefPubMedGoogle Scholar
  13. 13.
    Higuchi, T. Approach to an irregular time series on the basis of fractal theory. Phys. D 31:277–283, 1988.CrossRefGoogle Scholar
  14. 14.
    Korotchikova, I., S. Connolly, C. A. Ryan, D. M. Murray, A. Temko, B. R. Greene, and G. B. Boylan. EEG in the healthy term newborn within 12 hours of birth. Clin. Neurophysiol. 120:1046–1053, 2009.CrossRefPubMedGoogle Scholar
  15. 15.
    Lopes da Silva, F. H., A. Hoeks, H. Smits, and L. H. Zetterberg. Model of brain rhythmic activity: the alpha-rhythm of the thalamus. Kybernetik 15:27–37, 1974.CrossRefGoogle Scholar
  16. 16.
    Mizrahi, E., and P. Kellaway. Diagnosis and Management of Neonatal Seizure. Philadelphia: Lippincott-Raven, 1998.Google Scholar
  17. 17.
    Mizrahi, E. M., R. A. Hrachovy, and P. Kellaway. Atlas of Neonatal Electroencephalography, 3rd edn. Philadelphia: Lippincott, Williams and Wilkins, 2004.Google Scholar
  18. 18.
    Murray, D. M., G. B. Boylan, C. A. Ryan, and S. Connolly. Early EEG findings in hypoxic–ischaemic encephalopathy predict outcomes at 2 years. Pediatrics 124:e459–e467, 2009.CrossRefPubMedGoogle Scholar
  19. 19.
    Navakatikyan, M. A., P. B. Colditz, C. J. Burke, T. E. Inder, J. Richmond, and C. E. Williams. Seizure detection algorithm for neonates based on wave-sequence analysis. Clin. Neurophysiol. 117:1190–1203, 2006.CrossRefPubMedGoogle Scholar
  20. 20.
    Niedermeyer, E., and F. H. Lopes da Silva. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 5th edn. Philadelphia: Lippincott, Williams and Wilkins, 2004.Google Scholar
  21. 21.
    Notley, S. W. and S. J. Elliott. Efficient estimation of a time-varying dimension parameter and its application to EEG analysis. IEEE Trans. Biomed. Eng. 50:594–602, 2003.CrossRefGoogle Scholar
  22. 22.
    Oppenheim, A. V., R. W. Schafer, and J. R. Buck. Discrete-Time Signal Processing, 2nd edn. Upper Saddle River: Prentice Hall, 1999.Google Scholar
  23. 23.
    Peebles, P. Z., Jr. Probability, Random Variables and Random Signal Principles, 4th edn. Singapore: McGraw Hill, 2001.Google Scholar
  24. 24.
    Rankine, L. J., M. Mesbah, and B. Boashash. IF estimation for multicomponent signals using image processing techniques in the time-frequency domain. Signal Process. 87:1234–1250, 2007.CrossRefGoogle Scholar
  25. 25.
    Rankine, L., N. Stevenson, M. Mesbah, and B. Boashash. A nonstationary model of newborn EEG. IEEE Trans. Biomed. Eng. 54:19–28, 2007.CrossRefGoogle Scholar
  26. 26.
    Reklaitis, G. V., A. Ravindran, and K. M. Ragsdell. Engineering Optimization: Methods and Applications, 2nd edn. Hoboken: John Wiley & Sons, 2006.Google Scholar
  27. 27.
    Roessgen, M., A. Zoubir, and B. Boashash. Seizure detection of newborn EEG using a model–based approach. IEEE Trans. Biomed. Eng. :673–685, 1998.CrossRefGoogle Scholar
  28. 28.
    Roessgen, M. A. Analysis and Modelling of EEG Data with Application to Seizure Detection in the Newborn. PhD Dissertation, Brisbane: Queensland University of Technology, 1997.Google Scholar
  29. 29.
    Scher, M. S., B. L. Jones, D. A. Steppe, D. L. Cork, H. J. Seltman, and D. L. Banks. Functional brain maturation in neonates as measured by EEG-sleep analyses. Clin. Neurophysiol. 114:875–882, 2003.CrossRefPubMedGoogle Scholar
  30. 30.
    Shampine, L. F. Numerical Solution of Ordinary Differential Equations. New York: Chapman and Hall, 1994.Google Scholar
  31. 31.
    Srebro, R. The Duffing oscillator: a model for the dynamics of the neuronal groups comprising the transient evoked potential. Electroencephal. Clin. Neurophysiol. 96:561–573, 1995.CrossRefGoogle Scholar
  32. 32.
    Tahmasbi, R., and S. Rezaei. Change point detection in GARCH models for voice activity detection. IEEE Trans. Audio Speech. 16:1038–1046, 2008.CrossRefGoogle Scholar
  33. 33.
    Tuckwell, H. Introduction to Theoretical Neurobiology, Vol. 2. Cambridge: Cambridge University Press, 1988.Google Scholar
  34. 34.
    Vetterli, M., P., Marziliano, and T. Blu. Sampling signals with finite rate of innovation. IEEE Trans. Signal Proces. 50:1417–1428, 2002.CrossRefGoogle Scholar
  35. 35.
    Welch, P. D. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacous. AU-15:70–73, 1967.CrossRefGoogle Scholar
  36. 36.
    Zeeman. E. C. Brain modelling. In: Structural Stability, The Theory of Catastrophes, and Applications in the Sciences. Berlin: Springer, 1976, pp. 367–372.Google Scholar

Copyright information

© Biomedical Engineering Society 2010

Authors and Affiliations

  • N. J. Stevenson
    • 1
  • M. Mesbah
    • 2
  • G. B. Boylan
    • 1
  • P. B. Colditz
    • 2
  • B. Boashash
    • 2
    • 3
  1. 1.Neonatal Brain Research GroupUniversity College CorkCorkIreland
  2. 2.UQ Centre for Clinical ResearchUniversity of Queensland, Royal Brisbane and Women’s HospitalHerstonAustralia
  3. 3.College of EngineeringQatar UniversityDohaQatar

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