Electrical and Electronic Engineering - Research Publications

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Start date: 2014 × Author: Nesic, Dragan × Type: Abstract ×

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    Identification of a Neural Mass Model of Burst Suppression
    Jafarian, A ; Freestone, DR ; Nesic, D ; Grayden, D (IEEE, 2019)
    Burst suppression includes alternating patterns of silent and fast spike activities in neuronal activities observable in micro to macro scale recordings. Biological models of burst suppression are given as dynamical systems with slow and fast states. The aim of this paper is to give a method to identify parameters of a mesoscopic model of burst suppression that can provide insights into study underlying generators of intracranial electroencephalogram (iEEG) data. An optimisation technique based upon a genetic algorithm (GA) is employed to find feasible model parameters to replicate burst patterns in the iEEG data with paroxysmal transitions. Then, a continuous discrete unscented Kalman filter (CD-UKF) is used to infer hidden states of the model and to enhance the identification results from the GA. The results show promise in finding the model parameters of a partially observed mesoscopic model of burst suppression.