Changes in dynamical characteristics of epileptic EEG in rats using recurrence quantification analysis

机译：使用复制定量分析大鼠癫痫脑脑脑梗死的变化

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In this paper, we used Recurrence Quantification Analysis (RQA) in order to study pre-epileptic characteristics in rat's EEG recordings. Four adult rats were used to collect epileptic EEG data in an experiment of animal model of epilepsy. Three RQA measures, recurrence rate, determinism, and entropy were calculated from EEG recordings from rats. A moving average filter was used to identify the decreasing trend in pre-epileptic dynamics which will be useful early detection of seizures.

机译：在本文中，我们使用复发定量分析（RQA）以研究大鼠EEG记录中的癫痫表特征。四种成年大鼠用于收集癫痫动物模型的实验中的癫痫脑电图数据。从大鼠的EEG记录计算了三个RQA措施，复发率，确定性和熵。移动平均滤光器用于识别癫痫发育前动态的降低趋势，这将是有用的早期检测癫痫发作。

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《Annual International Conference of the IEEE Engineering in Medicine and Biology Society》|2011年||共4页
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Rabbi Ahmed F.; Jaiswal Manoj K.; Lei Saobo; Fazel-Rezai Reza;
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1. Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis [J] . Li XL, Ouyang GX, Yao X, Physics Letters, A . 2004,第1a2期

机译：癫痫发作前大鼠动态特征的复发定量分析
2. Dynamical analysis of epileptic characteristics based on recurrence quantification of SEEG recordings [J] . Yang Chuanzuo, Luan Guoming, Liu Zhao, Physica, A. Statistical mechanics and its applications . 2019,第期

机译：基于跷跷板录音复发定量的癫痫特性动态分析
3. AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS [J] . EE PING NG, TEIK-CHENG LIM, SUBHAGATA CHATTOPADHYAY, Journal of mechanics in medicine and biology . 2012,第5期

机译：使用递归定量分析自动识别癫痫和酒精性脑电信号
4. Changes in dynamical characteristics of epileptic EEG in rats using recurrence quantification analysis [C] . Rabbi Ahmed F., Jaiswal Manoj K., Lei Saobo, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society . 2011

机译：复发量化分析的大鼠癫痫脑电动力学特征的变化
5. Understanding Fecal Contamination Dynamics through the Integration of Molecular Pathogen Quantification and Land-Water Interface Characteristics [D] . ?Price, Matthew T. 2020

机译：通过集成分子病原体量化和陆路界面特性，了解粪便污染动力学
6. Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA) Diagonal-Cross-Recurrence Profiles (DCRP) and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R [O] . Sebastian Wallot, Giuseppe Leonardi -1

机译：使用交叉递归量化分析（CRQA）对角交叉递归分布图（DCRP）和多维递归量化分析（MdRQA）分析多元动力学– R中的教程
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）

Changes in dynamical characteristics of epileptic EEG in rats using recurrence quantification analysis

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