Seizure Reduction using Model Predictive Control

This study presents a model predictive control approach for seizure reduction in a computational model of epilepsy. The differential dynamic programming (DDP) algorithm is implemented in a model predictive fashion to optimize a controller for suppressing seizures in a chaotic oscillator model. The c...

Full description

Saved in:
Bibliographic Details
Main Authors: Brar, Harleen K., Exarchos, Ioannis, Pan, Yunpeng, Theodorou, Evangelos, Mahmoudi, Babak
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Correlation
Epilepsy
Heuristic algorithms
Optimal control
Oscillators
Predictive models
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study presents a model predictive control approach for seizure reduction in a computational model of epilepsy. The differential dynamic programming (DDP) algorithm is implemented in a model predictive fashion to optimize a controller for suppressing seizures in a chaotic oscillator model. The chaotic oscillator model uses proportional-integral (PI) controller to represent the internal control mechanism that maintains stable neural activity in a healthy brain. In the pathological case, the gains of this PI controller are reduced, preventing sufficient feedback to suppress correlation increase between normal and pathological brain dynamics. This increase in correlation leads to synchronization of oscillator dynamics leading to the destabilization of neural activity and epileptic behavior. The pathological case of the chaotic oscillator model is formulated as an optimal control problem, which we solve using the dynamic programming principle. We propose using model predictive control with differential dynamic programming optimization as a possible method for controlling epileptic seizures in known models of epilepsy.
ISSN:1558-4615
2694-0604
DOI:10.1109/EMBC.2018.8512911