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Epileptic headache: A cephalic pain seizure, confirmed by the close coincidence with epileptiform EEG abnormalities

机译：癫痫性头痛：癫痫性头痛，由癫痫样脑电图异常重合所证实

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《Seizure: the journal of the British Epilepsy Association》 |2016年第null期|共2页
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Cianchetti Carlo;
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正文语种 eng
中图分类神经病学与精神病学;
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1. Epileptic headache: A cephalic pain seizure, confirmed by the close coincidence with epileptiform EEG abnormalities [J] . Cianchetti Carlo Seizure: the journal of the British Epilepsy Association . 2016,第Null期

机译：癫痫性头痛：癫痫性头痛，由癫痫样脑电图异常重合所证实
2. The cognitive effects of interictal epileptiform EEG discharges and short nonconvulsive epileptic seizures [J] . NicolaiJ., EbusS., BiemansD.P.L.J.J.G., Epilepsia: Journal of the International League against Epilepsy . 2012,第6期

机译：发作性癫痫样脑电图放电和短暂的非惊厥性癫痫发作的认知作用
3. The cognitive impact of epileptiform EEG discharges and short epileptic seizures: relationship to characteristics of the cognitive tasks. [J] . Aldenkamp AP, Arends J, de la Parra NM, Epilepsy & behavior: E&B . 2010,第2期

机译：癫痫样脑电图放电和短暂癫痫发作的认知影响：与认知任务特征的关系。
4. Epileptic seizure classification from intracranial EEG signals: A comparative study EEG-based seizure classification [C] . Md. Khayrul Bashar, Faruque Reza, Zamzuri Idris, 2016 IEEE-EMBS Conference on Biomedical Engineering and Sciences . 2016

机译：颅内脑电图信号的癫痫性癫痫发作分类：基于脑电图的癫痫发作分类的比较研究
5. Multi-Feature Analysis of Eeg Signal on Seizure Patterns and Deep Neural Structures for Prediction of Epileptic Seizures [D] . Ma, Xinyuan. 2020

机译：癫痫发作模式和深神经结构对癫痫癫痫发作预测的多种特征分析
6. Surgical outcome of a female patient with comorbid epilepsy and psychogenic non-epileptic seizures confirmed with video-EEG monitoring [O] . Liang Qiao, Tao Yu, Yongjie Li 2021

机译：用Video-EEG监测证实了同血管癫痫和心理非癫痫发作的女性患者的手术结果
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. Epileptic Seizures from Abnormal Networks: Why Some Seizures Defy Predictability. [R] . Anderson, W. S., Azhar, F., Kudela, P., 2011

机译：异常网络的癫痫发作：为什么有些癫痫发作会破坏可预测性。

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