Model Predictive Control for Seizure Suppression Based on Nonlinear Auto-Regressive Moving-Average Volterra Model

Siyuan Chang, Xile Wei, Fei Su, Chen Liu, Guosheng Yi, Jiang Wang, Chunxiao Han, Yanqiu Che

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This article investigates a closed-loop brain stimulation method based on model predictive control strategy to suppress epileptic seizures. A neural mass model (NMM), exhibiting the normal and various epileptic seizures by changing physiologically meaningful parameters, is used as a black-box model of the brain. Based on system identification, an auto-regressive moving-average Volterra model is established to reveal the relationship between stimulation and neuronal responses. Then, the model predictive control strategy is implemented based the Volterra model, which can generate an optimal stimulation waveform to eliminate epileptiform waves. The computational simulation results indicate the proposed closed-loop control strategy can optimize the stimulation waveform without particular knowledge of the physiological properties of the system. The robustness of the proposed control strategy to system disturbances makes it more appropriate for future clinical application.

Original languageEnglish (US)
Article number9162059
Pages (from-to)2173-2183
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume28
Issue number10
DOIs
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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