Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy

Honghui Zhang, Jianzhong Su, Qingyun Wang, Yueming Liu, Levi Good, Juan M. Pascual

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

Original languageEnglish (US)
Pages (from-to)330-343
Number of pages14
JournalCommunications in Nonlinear Science and Numerical Simulation
Volume56
DOIs
StatePublished - Mar 2018

Keywords

  • EEG
  • Inherited seizures
  • Seizure prediction
  • Synaptic plasticity

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Applied Mathematics

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