Abstract
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.
Similar content being viewed by others
References
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.
Aminoff, M. J. Electrodiagnosis in Clinical Neurology. New York: Churchill Livingstone, 1992.
Boashash, B., E. J. Powers, and A. M. Zoubir. Higher Order Statistical Signal Processing. Melbourne: Longman Australia, 1995.
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.
Celka, P., B. Boashash, and P. Colditz. Preprocessing and time–frequency analysis of newborn EEG seizures. IEEE Eng. Med. Biol. 20:30–39, 2001.
Celka, P., and P. Colditz. Nonlinear nonstationary Wiener model of infant EEG seizures. IEEE Trans. Biomed. Eng. 49:556–564, 2002.
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.
Conover, W. J. Practical Nonparametric Statistics, 3rd edn. New York: Wiley, 1999.
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.
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.
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.
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, 2008
Higuchi, T. Approach to an irregular time series on the basis of fractal theory. Phys. D 31:277–283, 1988.
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.
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.
Mizrahi, E., and P. Kellaway. Diagnosis and Management of Neonatal Seizure. Philadelphia: Lippincott-Raven, 1998.
Mizrahi, E. M., R. A. Hrachovy, and P. Kellaway. Atlas of Neonatal Electroencephalography, 3rd edn. Philadelphia: Lippincott, Williams and Wilkins, 2004.
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.
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.
Niedermeyer, E., and F. H. Lopes da Silva. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 5th edn. Philadelphia: Lippincott, Williams and Wilkins, 2004.
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.
Oppenheim, A. V., R. W. Schafer, and J. R. Buck. Discrete-Time Signal Processing, 2nd edn. Upper Saddle River: Prentice Hall, 1999.
Peebles, P. Z., Jr. Probability, Random Variables and Random Signal Principles, 4th edn. Singapore: McGraw Hill, 2001.
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.
Rankine, L., N. Stevenson, M. Mesbah, and B. Boashash. A nonstationary model of newborn EEG. IEEE Trans. Biomed. Eng. 54:19–28, 2007.
Reklaitis, G. V., A. Ravindran, and K. M. Ragsdell. Engineering Optimization: Methods and Applications, 2nd edn. Hoboken: John Wiley & Sons, 2006.
Roessgen, M., A. Zoubir, and B. Boashash. Seizure detection of newborn EEG using a model–based approach. IEEE Trans. Biomed. Eng. :673–685, 1998.
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.
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.
Shampine, L. F. Numerical Solution of Ordinary Differential Equations. New York: Chapman and Hall, 1994.
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.
Tahmasbi, R., and S. Rezaei. Change point detection in GARCH models for voice activity detection. IEEE Trans. Audio Speech. 16:1038–1046, 2008.
Tuckwell, H. Introduction to Theoretical Neurobiology, Vol. 2. Cambridge: Cambridge University Press, 1988.
Vetterli, M., P., Marziliano, and T. Blu. Sampling signals with finite rate of innovation. IEEE Trans. Signal Proces. 50:1417–1428, 2002.
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.
Zeeman. E. C. Brain modelling. In: Structural Stability, The Theory of Catastrophes, and Applications in the Sciences. Berlin: Springer, 1976, pp. 367–372.
Author information
Authors and Affiliations
Corresponding author
Additional information
Associate Editor Larry V. McIntire oversaw the review of this article.
Rights and permissions
About this article
Cite this article
Stevenson, N.J., Mesbah, M., Boylan, G.B. et al. A Nonlinear Model of Newborn EEG with Nonstationary Inputs. Ann Biomed Eng 38, 3010–3021 (2010). https://doi.org/10.1007/s10439-010-0041-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-010-0041-3