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Distinguish Method of Epileptic EEG and Deglutition EEG Based on Chaotic Noise-Reduction.

机译：基于混沌降噪的癫痫脑电和吞咽脑电图鉴别方法。

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A new method for recovering epileptic EEG and deglutition EEG's nonnoise trajectory and distinguishing these two waveforms is presented. The main aim of this paper is to introduce the theory and establish math model of a simple recovering EEG's nonnoise trajectory. This method is finally used to experiment. Our results prove that chaotic dynamics does exist in EEG and signal-noise of EEG marked improves. Furthermore, it is also successful to recover structural characteristic of strange attractor destroyed by noise. The paper obtains the real chaotic trajectory of EEG with which it can calculate dipole of EEG. According to the different parameters of dipole, a method of distinguishing epileptic EEG and deglutition EEG using the measurement of nonlinear dynamics is obtained.

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作者
Ouyang, G.; Li, C.; Jiang, G.;
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年度 2001
页码 1-5
总页数 5
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Nonlinear analysis; Electroencephalography; Signal processing; Chaos; Noise reduction; China; Epilepsy; Foreign reports;

机译：非线性分析;脑电图;信号处理;混沌;降噪;中国;癫痫;外国报道;

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3. Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods [J] . Aci Cigdem Ivan, Kaya Murat, Mishchenko Yuriy Expert Systems with Application . 2019,第NOV期

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4. A distinguish method of epileptic EEG and deglutition EEG based on chaotic noise-reduction [C] . Guanghua Ouyang, Chunyan Li, Guotai Jiang Annual International Conference of the IEEE Engineering in Medicine and Biology Society . 2001

机译：基于混沌降噪的癫痫脑脑脑脑脑脑脑筋分辨率
5. Monitoring the depth of anesthesia: Methods based on EEG signal processing. [D] . Tan, Zhibin. 2007

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6. A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG [O] . Mengran Zhou, Kai Bian, Feng Hu, 2020

机译：一种基于CEEMD结合迭代特征降低的新方法疏远癫痫症的辅助诊断
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）

Distinguish Method of Epileptic EEG and Deglutition EEG Based on Chaotic Noise-Reduction.

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