Parameter pattern discovery in nonlinear dynamic model for EEGs analysis

Sun Hee Kirn, Christos Faloutsos, Hyung Jeong Yang, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


We propose a nonlinear dynamic model for an invasive electroencephalogram analysis that learns the optimal parameters of the neural population model via the Levenberg-Marquardt algorithm. We introduce the crucial windows where the estimated parameters present patterns before seizure onset. The optimal parameters minimizes the error between the observed signal and the generated signal by the model. The proposed approach effectively discriminates between healthy signals and epileptic seizure signals. We evaluate the proposed method using an electroencephalogram dataset with normal and epileptic seizure sequences. The empirical results show that the patterns of parameters as a seizure approach and the method is efficient in analyzing nonlinear epilepsy electroencephalogram data. The accuracy of estimating the optimal parameters is improved by using the nonlinear dynamic model.

Original languageEnglish
Pages (from-to)381-402
Number of pages22
JournalJournal of Integrative Neuroscience
Issue number3
Publication statusPublished - 2016 Sept 1

Bibliographical note

Publisher Copyright:
© World Scientific Publishing Europe Ltd.


  • Epileptic seizure
  • electroencephalogram
  • neurons population
  • nonlinear dynamic model
  • parameter changes

ASJC Scopus subject areas

  • General Neuroscience


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