April 2000: A 43 year old male with generalized epileptic seizures.

Miklossy J; Kopniczky Z; Uske A; Delacretaz F; Chaubert P; Porchet F

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April 2000: A 43 year old male with generalized epileptic seizures.

机译：2000年4月：一名43岁的男性，患有全身性癫痫发作。

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《Brain pathology》 |2000年第3期|共2页
作者
Miklossy J; Kopniczky Z; Uske A; Delacretaz F; Chaubert P; Porchet F;
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正文语种 eng
中图分类病理学;
关键词
Epilepsy; Meningeal Neoplasms; Meningioma; 癫痫; 脑膜肿瘤; 脑膜瘤;

机译：Epilepsy;Meningeal Neoplasms;Meningioma;癫痫;脑膜肿瘤;脑膜瘤;

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1. April 2000: A 43 year old male with generalized epileptic seizures. [J] . Miklossy J, Kopniczky Z, Uske A, Brain pathology . 2000,第3期

机译：2000年4月：一名43岁的男性，患有全身性癫痫发作。
2. Patterns of muscle activation during generalized tonic and tonic-clonic epileptic seizures. [J] . Conradsen I, Wolf P, Sams T, Epilepsia: Journal of the International League against Epilepsy . 2011,第11期

机译：全身性强直性和强直性阵挛性癫痫发作期间的肌肉激活模式。
3. Altered regional synchronization in epileptic patients with generalized tonic-clonic seizures. [J] . Zhong Y, Lu G, Zhang Z, Epilepsy research . 2011,第1a2期

机译：全身性强直阵挛性癫痫发作的癫痫患者的区域同步性改变。
4. MINUTES AFS FIRST BOARD OF DIRECTORS MEETING -2000-2001(Pittsburgh Hilton Towers, Pittsburgh Pennsylvania-April 11, 2000) [C] . CHARLES H. JONES, DONALD E. GAERTNER American Foundrymen's Society Conference . 2000

机译：分钟AFS第一委员会会议-2000-2001（匹兹堡希尔顿省和塔楼，匹兹堡宾夕法尼亚州 - 2000年4月11日）
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. Research Strategies for Health. Based on the Technical Discussions at the 43rd World Health Assembly on the `Role of Health Research in the Strategy for Health for All by the Year 2000 [O] . J Powles 1994

机译：健康研究策略。基于第43届世界卫生大会关于健康研究在2000年人人享有健康战略中的作用的技术讨论
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）
8. EEG-Based Classification and Advanced Warning of Epileptic Seizures. [R] . Clisby, L. E. 2016

机译：基于脑电图的癫痫发作分类和预警。

April 2000: A 43 year old male with generalized epileptic seizures.

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