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Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures

机译：动态相互作用决定了部分癫痫发作在计算机网络波峰癫痫发作模型中的作用

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William W. Lytton; Diego Contreras; Alain Destexhe; Mircea Steriade; William W;
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年度 1997
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1. Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures. [J] . Lytton WW, Contreras D, Destexhe A, Journal of Neurophysiology . 1997,第4期

机译：动态相互作用决定了峰值和波浪发作的计算机网络模型中的部分丘脑静止。
2. Developmental changes of GABA immunoreactivity in cortico-thalamic networks of an absence seizure model [J] . Bombardi Cristiano, Venzi Marcello, Crunelli Vincenzo, Neuropharmacology . 2018,第Pta1期

机译：缺乏癫痫发作模型的皮质粒网络中GABA免疫反应性的发育变化
3. Medium-voltage 5-9-Hz oscillations give rise to spike-and-wave discharges in a genetic model of absence epilepsy: in vivo dual extracellular recording of thalamic relay and reticular neurons. [J] . Pinault D, Vergnes M, Marescaux C Neuroscience: An International Journal under the Editorial Direction of IBRO . 2001,第1期

机译：在没有癫痫病的遗传模型中，中压5-9Hz振荡会引起波峰放电：丘脑中枢和网状神经元的体内双细胞外记录。
4. Modeling cortical source dynamics and interactions during seizure [C] . Mullen Tim, Acar Zeynep Akalin, Worrell Gregory, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society . 2011

机译：癫痫发作期间皮质源动力学和相互作用的建模
5. Modeling, Analysis and Mitigation of Sub-Synchronous Interactions between Full- and Partial-Scale Voltage-Source Converters and Power Networks. [D] . Alawasa, Khaled Mohammad. 2014

机译：满量程和部分量程电压源转换器与电网之间次同步相互作用的建模，分析和缓解。
6. Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators [O] . Björn R. H. Blomqvist, Richard P. Mann, David J. T. Sumpter 2012

机译：使用贝叶斯动力学系统，模型平均和神经网络来确定社会经济指标之间的相互作用
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）

Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures

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