Model-based robust suppression of epileptic seizures without sensory measurements

机译：基于模型的癫痫发作的鲁棒抑制没有感觉测量

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Parameters of the cortex model (Steyn-Ross et al. 2003)

机译：皮质模型的参数（Steyn-Ross等，2003）

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期刊名称 Cognitive Neurodynamics
作者
Meriç Çetin;
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作者单位

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年(卷),期 2020(14),1
年度 2020
页码 51–67
总页数 17
原文格式 PDF
正文语种
中图分类神经科学;
关键词

机译：皮质模型;癫痫癫痫发作;不确定的动态;Takagi-sugeno模糊建模;基于观察者的稳定;PID控制;

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专利

1. Model-based robust suppression of epileptic seizures without sensory measurements [J] . Cetin Meric Cognitive Neurodynamics . 2020,第1期

机译：基于模型的癫痫发作的鲁棒抑制，没有感觉测量
2. Neonatal suppression-burst without epileptic seizures: Expanding the electroclinical phenotype of STXBP1-related, early-onset encephalopathy [J] . MastrangeloM., PeronA., SpacciniL., Epileptic disorders: international epilepsy journal with videotape . 2013,第1期

机译：无癫痫发作的新生儿抑制性猝发：扩大与STXBP1相关的早发性脑病的电子临床表型
3. Prevalence of alexithymia in patients with psychogenic non-epileptic seizures and epileptic seizures and predictors in psychogenic non-epileptic seizures [J] . MyersL., MatznerB., LancmanM., Epilepsy & behavior: E&B . 2013,第2期

机译：心因性非癫痫性癫痫和癫痫发作患者的运动障碍症患病率及心因性非癫痫性癫痫发作的预测因子
4. An implementation of a wavelet-based seizure detection filter suitable for realtime closed-loop epileptic seizure suppression [C] . van Dongen M.N., Karapatis A., Kros L., IEEE biomedical circuits and systems conference . 2014

机译：适用于实时闭环癫痫发作抑制的基于小波的癫痫发作检测滤波器的实现
5. Improving the Ability of the MMPI-2-RF to Discriminate between Psychogenic Non-epileptic Seizures and Epileptic Seizures. [D] . Wershba, Rebecca E. 2013

机译：提高MMPI-2-RF区分精神性非癫痫性发作和癫痫性癫痫发作的能力。
6. A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures [O] . Antonio Quintero-Rincón, Carlos DGiano, Hadj Batatia 2020

机译：基于二次线性抛物线模型的EEG分类以检测癫痫发作
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Model-based robust suppression of epileptic seizures without sensory measurements

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