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Seizure control in a neural mass model by an active disturbance rejection approach

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收录情况: ◇ SCIE ◇ EI

机构: [1]Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China [2]Capital Med Univ, Xuanwu Hosp, Beijing Inst Funct Neurosurg, Beijing, Peoples R China [3]Chinese Acad Sci, Shenyang Inst Automat, Inst Robot & Intelligent Mfg, State Key Lab Robot, Nanta 114, Shenyang 110016, Liaoning, Peoples R China
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关键词: Closed-loop neuromodulation epilepsy the neural mass model active disturbance rejection control

摘要:
A closed-loop neuromodulation automatically adjusts stimuli according to brain response in real time. It is viewed as a promising way to control medically intractable epilepsy. A suitable closed-loop modulation strategy, which is robust enough to unknown nonlinearities, dynamics, and disturbances, is in great need in the clinic. For the specialization of epilepsy, the Jansen's neural mass model is utilized to simulate the undesired high amplitudes epileptic activities, and active disturbance rejection control is designed to suppress the high amplitudes of epileptiform discharges. With the help of active disturbance rejection control, closed-loop roots of the system are far from the imaginary axis. Time domain response shows that active disturbance rejection control is able to control seizure no matter whether disturbances exist or not. At the same time, frequency domain response presents that enough stability margins and a broader range of tunable controller parameters can be obtained. Stable regions have also been presented to provide guidance to choose the parameters of active disturbance rejection control. Numerical results show that, compared with proportional-integral control, more accurate modulation with less energy can be achieved by active disturbance rejection control. It confirms that the active disturbance rejection control-based neuromodulation solution is able to achieve a desired performance. It is a promising closed-loop neuromodulation strategy in seizure control.

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出版当年[2018]版:
大类 | 4 区 工程技术
小类 | 4 区 机器人学
最新[2025]版:
大类 | 4 区 计算机科学
小类 | 4 区 机器人学
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出版当年[2017]版:
Q4 ROBOTICS
最新[2023]版:
Q3 ROBOTICS

影响因子: 最新[2023版] 最新五年平均 出版当年[2017版] 出版当年五年平均 出版前一年[2016版] 出版后一年[2018版]

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第一作者机构: [1]Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China
通讯作者:
通讯机构: [3]Chinese Acad Sci, Shenyang Inst Automat, Inst Robot & Intelligent Mfg, State Key Lab Robot, Nanta 114, Shenyang 110016, Liaoning, Peoples R China
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