An analysis of EEG signal generated from ischemic stroke patient

机译：缺血性中风患者产生的脑电信号分析

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This paper presents the analysis of an Electroencephalogram (EEG) signal generated from ischemic stroke patient. The EEG signal were recorded from two channel; Ch. A and Ch. B and filtered using band pass filter with frequency range of 0 Hz to 30 Hz. Fast Fourier Transform (FFT) was used to analysis the signal. Analysis of EEG signals showed variation of pattern belongs to each of every sub band in this brainwaves activity for each subject. EEG processing techniques contribute to the understanding of the EEG brain processes and search for its physiological significance.

机译：本文介绍了缺血性中风患者产生的脑电图（EEG）信号的分析。脑电信号是从两个通道记录的。频道A和Ch。 B，然后使用带通滤波器进行滤波，频率范围为0 Hz至30 Hz。快速傅立叶变换（FFT）用于分析信号。对脑电信号的分析表明，该模式的变化属于每个对象在此脑电波活动中每个子带中的每个子带。脑电图处理技术有助于了解脑电图的大脑过程，并寻求其生理意义。

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来源
《Proceedings 2015 Innovation amp; Commercialization of Medical Electronic Technology Conference》|2015年|74-77|共4页
会议地点 Selangor(MY)
作者
W. R. W. Omar; Z. Mohamad; M. N. Taib; R. Jailani;
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作者单位

Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Selangor, MALAYSIA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Electroencephalogram (EEG); Frequency analysis; stroke;

机译：脑电图;频率分析;中风;

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1. The use of routine EEG in acute ischemic stroke patients without seizures: generalized but not focal EEG pathology is associated with clinical deterioration [J] . Wolf Marc E., Ebert Anne D., Chatzikonstantinou Anastasios International Journal of Neuroscience . 2017,第1a6期

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4. An analysis of EEG signal generated from ischemic stroke patient [C] . W. R. W. Omar, Z. Mohamad, M. N. Taib, Innovation Commercialization of Medical Electronic Technology Conference . 2015

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5. An analysis of the relationship between hyperglycemia and the National Institute of Health Stroke Scale (NIHSS) scores in ischemic stroke patients. [D] . Bohl, Susan. 2010

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6. Epistasis Analysis for Estrogen Metabolic and Signaling Pathway Genes on Young Ischemic Stroke Patients [O] . Yi-Chen Hsieh, Jiann-Shing Jeng, Huey-Juan Lin, -1

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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）

An analysis of EEG signal generated from ischemic stroke patient

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