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Multiresolution area-based fractal dimension estimation of signals applied to EEG data

机译：基于多分辨率区域的分形维数估计应用于脑电数据的信号

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《TENCON 2008 - 2008 IEEE Region 10 Conference》||P.1-5|共5页
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Raghavendra B. S.; Narayana Dutt D.;
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1. Surrogate data modeling the relationship between high frequency amplitudes and Higuchi fractal dimension of EEG signals in anesthetized rats [J] . Sladjana Spasic, Aleksandar Kalauzi, Srdjan Kesic, Journal of Theoretical Biology . 2011,第3期

机译：替代数据模拟麻醉大鼠脑电信号高频振幅与dimension口分形维数之间的关系
2. Surrogate data modeling the relationship between high frequency amplitudes and Higuchi fractal dimension of EEG signals in anesthetized rats [J] . Sladjana Spasic, Aleksandar Kalauzi, Srdjan Kesic, Journal of Theoretical Biology . 2011,第3期

机译：代理数据建模麻醉大鼠高频幅度与脑电坡脑电图信号分数尺寸的关系
3. Surrogate data modeling the relationship between high frequency amplitudes and Higuchi fractal dimension of EEG signals in anesthetized rats [J] . Sladjana Spasic, Aleksandar Kalauzi, Srdjan Kesic, Journal of Theoretical Biology . 2011,第3期

机译：代理数据建模麻醉大鼠高频幅度与脑电坡脑电图信号分数尺寸的关系
4. Multiresolution area-based fractal dimension estimation of signals applied to EEG data [C] . Raghavendra B. S., Narayana Dutt D. IEEE Region 10 Conference . 2008

机译：基于多分辨率的区域的分形尺寸估计信号应用于EEG数据的信号
5. PATTERN RECOGNITION AND SIGNAL PROCESSING TECHNIQUES APPLIED TO EEG MOTORSIGNAL ANALYSIS. [D] . NIRENBERG, LLOYD MICHAEL. 1969

机译：模式识别和信号处理技术应用于脑电信号分析。
6. Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies [O] . R. Salazar-Varas, Roberto A. Vazquez 2019

机译：在使用分形维数和不同截止频率的分类过程中面对高EEG信号的变异性
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）

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