Abstract
Epilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need. In this paper, active disturbance rejection control (ADRC) is utilized for its satisfied disturbance rejection and regulation performance. However, fixed observer parameters are difficult to fit the time-varying electrophysiological signals. Therefore, based on the estimation errors, an iterative learning approach is designed to get the parameters of an extended state observer (ESO). By combining the advantages of ADRC and the iterative learning, a learning type ADRC (LTADRC) is proposed to suppress the high amplitude epileptiform waves generated by the Jansen's neural mass model (NMM). For those variable parameters of an ESO, scalable bandwidths can be obtained to adapt to time-varying disturbance signals. It is of great significance for both ADRC and the neuro-modulation of epilepsy. Simulation results show that, compared with ADRC, much better performance can be obtained. It may provide a promising closed-loop regulation way for epilepsy in clinics.
Highlights
Epilepsy, as a common neurological disease with a high incidence, has a great impact on over 70 million people over the world [1]
The closed-loop neuro-modulation has become a trend in the treatment of epilepsy [13], and numerous efforts have been made on such topic, such as PID control [4],[14], feedback linearization control [15], fuzzy PID control [16], closed-loop iterative learning control (ILC) based on unscented Kalman filter (UKF) [17] and parameter estimation and control based on particle swarm optimization (PSO) [18]
To suppress the epileptic behaviors, both active disturbance rejection control (ADRC) and learning type ADRC (LTADRC) based on P-type iterative learning are introduced
Summary
As a common neurological disease with a high incidence, has a great impact on over 70 million people over the world [1]. The closed-loop neuro-modulation has become a trend in the treatment of epilepsy [13], and numerous efforts have been made on such topic, such as PID control [4],[14], feedback linearization control [15], fuzzy PID control [16], closed-loop iterative learning control (ILC) based on unscented Kalman filter (UKF) [17] and parameter estimation and control based on particle swarm optimization (PSO) [18]. We can see clearly that a control strategy, which is able to suppress epilepsy actively and effectively even in absence of enough information on epileptic model and disturbances, is necessary in clinic. Not based on a concrete model, but based on estimating and compensating the uncertainties, active disturbance rejection control (ADRC), proposed by Han in 1990s, is able to achieve desired performance [19].
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