Correlation dimension to determine changes in subbands of epileptic signals

机译：相关维度确定癫痫信号子带的变化

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Epilepsy is a widely spread neurological disorder, caused due to the abnormal electrical activity in the brain. There is a significant change in the functioning and the dynamics of the brain due to this disorder. In this work, we have investigated the dynamics by analyzing the dimension changes observed with respect to four classes namely; normal-eye open, normal-eye close, epilepsy-without seizure and epilepsy- with seizure. Correlation dimension and fuzzy based correlation dimension of the signal is determined to analyze the dynamical changes in the four classes. Finally, the signals were classified using support vector machine (SVM) and 4-nearest neighborhood (4-NN) with a classification accuracy of 94.5% and 93.5% respectively.

机译：癫痫是一种广泛的展开神经障碍，由于大脑中的电气活动异常引起。由于这种疾病，大脑的功能和动态存在显着变化。在这项工作中，我们通过分析了四个类别观察到的尺寸变化来调查动态;正常眼睛开放，正常眼睛关闭，癫痫 - 没有癫痫发作和癫痫癫痫发作。确定信号的相关尺寸和基于模糊的相关尺寸，以分析四种类中的动态变化。最后，使用支持向量机（SVM）和4最近的邻域（4-NN）分类信号分别分别为94.5％和93.5％。

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《IEEE Global Conference on Life Sciences and Technologies》|2019年|301p|共4页
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作者
Prajna Upadhyaya; Tohru Yagi;
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原文格式 PDF
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中图分类 R318-53;
关键词
Epilepsy; Epileptiform discharges; EEG; Seizure;

机译：癫痫;癫痫株;脑电图;癫痫发作;

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专利

1. Structural and subband changes at the LaAlO3/SrTiO3 interface investigated by two-dimensional correlation spectroscopy [J] . Xin Feng, Kun Zhao, Li-Zhi Xiao, AIP Advances . 2015,第6期

机译：二维相关光谱法研究LaAlO3 / SrTiO3界面处的结构和子带变化
2. Fuzzy Mutual Information as a Dimensionality Reduction Technique for Epileptic Electroencephalography Signals [J] . R. Harikumar, P. Sunil Kumar Research journal of applied science, engineering and technology . 2015,第9期

机译：模糊互信息作为癫痫脑电信号的降维技术
3. Dimensionality Reduction with Linear Graph Embedding Technique for Electroencephalography Signals of an Epileptic Patient [J] . R. Harikumar, P. Sunil Kumar Research journal of pharmacy and technology . 2015,第5期

机译：线性图嵌入技术对癫痫患者脑电信号的降维
4. Correlation dimension to determine changes in subbands of epileptic signals [C] . Prajna Upadhyaya, Tohru Yagi IEEE Global Conference on Life Sciences and Technologies . 2019

机译：相关维度确定癫痫信号子带的变化
5. Two-dimensional autoregressive modeling by autocorrelation fitting and applications to signal processing. [D] . Chen, Haiguang. 1991

机译：通过自相关拟合进行二维自回归建模并将其应用于信号处理。
6. A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis [O] . Gaowei Xu, Tianhe Ren, Yu Chen, 2020

机译：eEG信号分析的癫痫癫痫识别的一维CNN-LSTM模型
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）

Correlation dimension to determine changes in subbands of epileptic signals

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