Seizure Control by a Learning Type Active Disturbance Rejection Approach

Wei, Wei; Wei, Xiaofang; Xia, Pengfei; Zuo, Min; Shen, Dong

doi:10.1109/access.2019.2948943

Cited by 6 publications

(2 citation statements)

References 30 publications

(36 reference statements)

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“…The use of neural mass models akin to the Jansen-Rit model in feedback control frameworks is well documented. The works in [66,[73][74][75][76][77] all use neural mass models from the point of view of control theory for the suppression of epileptic seizures. In what follows, we will demonstrate the effectiveness of our proposed control strategy on a seizure simulated by the classical Jansen-Rit neural mass model with standard parameter values-see Supplementary Material.…”

Section: Epileptic Seizure Simulated By the Jansen-rit Neural Mass Mo...mentioning

confidence: 99%

Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy

et al. 2020

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Objective. Electrical neurostimulation is an increasingly adopted therapeutic methodology for neurological conditions such as epilepsy. Electrical neurostimulation devices are commonly characterized by their limited sensing, actuating, and computational capabilities. However, the sensing mechanisms are often used only for their detection potential (e.g. to detect seizures), which automatically and dynamically trigger the actuation capabilities, but ultimately deploy prespecified stimulation doses that resulted from a period of manual (and empirical) calibration. The potential information contained in the measurements acquired by the sensing mechanisms is, therefore, considerably underutilized, given that this type of stimulation strategy only entails an event-triggered relationship between the sensors and actuators of the device. Such stimulation strategies are suboptimal in general and lack theoretical guarantees regarding their performance. Approach. In order to leverage the aforementioned information, harvested during normal sensing-actuating operation, we must consider a real-time feedback (closed-loop) strategy. More precisely, the stimulation signal itself should automatically adapt based upon the state of the neurophysiological system at hand, estimated from data collected in real-time through sensors in the device. Main results. In this work, we propose a model-based approach for (real-time) closed-loop electrical neurostimulation, in which the evolution of the system is captured by a fractional-order system (FOS). More precisely, we propose a model predictive control (MPC) approach with an underlying FOS predictive model, due to the ability of fractional-order dynamics to more accurately capture the long-term dependence present in biological systems, compared to the standard linear time-invariant models. Furthermore, MPC offers, by design, an additional layer of robustness to compensate for system-model mismatch, which the more traditional strategies lack. To establish the potential of our framework, we focus on epileptic seizure mitigation by computational simulation of our proposed strategy upon seizure-like events. Lastly, we provide evidence of the effectiveness of our method on seizures simulated by commonly adopted models in the neuroscience and medical community present in the literature, as well as real seizure data as obtained from subjects with epilepsy. Significance Our study thus paves the way for the development and implementation of robust real-time closed-loop electrical neurostimulation which can then be used for the construction of more effective devices for epileptic seizure mitigation.

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Section: Epileptic Seizure Simulated By the Jansen-rit Neural Mass Mo...mentioning

confidence: 99%

Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy

et al. 2020

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“…However, to some extent, determining parameters of an ESO depends on engineers’ experience. To make the ESO be more accurate in estimating and reducing the dependence on experience to fix the parameters, a P type iterative learning algorithm 20 is utilized, and the iterative learning based ADRC (ILADRC) 21 is designed in the DO concentration control. Main advantage of the ILADRC is that the ESO in the ILADRC can acquire better estimation performance by an iterative learning approach.…”

Section: Introductionmentioning

confidence: 99%

Disturbance rejection control for a wastewater treatment process by a learning approach

Wei

Chen

Liu

et al. 2020

Measurement and Control

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Nonlinearities, uncertainties and external disturbances commonly exist in a wastewater treatment process (WWTP). Those issues present great challenges to the control of the dissolved oxygen (DO) concentration in a WWTP. In this paper, an active disturbance rejection control (ADRC) is utilized to estimate the total disturbance and drive the DO concentration to track the set-value. Simultaneously, an iterative learning strategy is employed to adjust the parameters of an extended state observer (ESO) to improve the accuracy of the estimation and reduce the dependence on experience in determining parameters. By combining the advantages of the ADRC and the iterative learning strategy, an iterative learning based active disturbance rejection control (ILADRC) is constructed, and the close-loop stability is analyzed. The benchmark simulation model No.1 (BSM1) is utilized to confirm the ILADRC. Numerical results show that the ILADRC is more effective in the DO concentration control.

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