ArticleCommunications in nonlinear science & numerical simulation2018
Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy.
Article in Communications in nonlinear science & numerical simulation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 25 citations in OpenAlex.
- Modular Model of Neuronal Activity That Captures the Dynamics of Main Molecular Targets of Antiepileptic Drugs.International journal of molecular sciences · 2026Article
- Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states.Journal of biomedical research · 2020Article
- A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures.Journal of biomedical research · 2019Article
- Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion.Frontiers in neuroinformatics · 2018Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
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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.
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