Removal of muscle artifacts from EEG based on ensemble empirical mode decomposition and classification of seizure using machine learning techniques

机译：基于整体经验模式分解和癫痫发作分类的脑电信号去除方法，使用机器学习技术

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Occurrence of sudden burst of excess electricity in the brain, manifesting as seizure is common phenomenon observed in patients with epilepsy: a neurological disorder that affects approximately 70 million people in the world. The epilepsy mainly divided into two types - Partial and Generalized. Electroencephalograms (EEG) recordings can capture the brain's electrical signals, but diagnosis of epilepsy and identifying its correct class is time consuming and can be expensive due to the need for trained specialists to perform the interpretation, because of the nature of EEG signal, which normally get contaminated by noises and artifacts (signals other than brain activity), which affects the visual analysis of EEG and impairs the results of EEG signal processing. We present de-noising of EEG signal using Ensemble Empirical Mode Decomposition (EEMD) and classification based on machine learning Technique using MATLAB.

机译：在癫痫患者中常见的现象是大脑中突然出现过剩的电量，表现为癫痫发作：这是一种神经病，影响全世界约7000万人。癫痫病主要分为两种：部分性和广义性。脑电图（EEG）记录可以捕获大脑的电信号，但是由于EEG信号的性质，通常需要训练有素的专家来进行解释，诊断癫痫和确定其正确的分类非常耗时，并且可能很昂贵。被噪音和伪影（除大脑活动以外的信号）污染，这会影响脑电图的视觉分析并损害脑电信号处理的结果。我们提出了基于整体经验模式分解（EEMD）的EEG信号降噪和基于使用MATLAB的机器学习技术进行分类的方法。

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来源
《2017 International Conference on Inventive Computing and Informatics》|2017年|861-866|共6页
会议地点 Coimbatore(IN)
作者
Kusumika Krori Dutta; Kavya Venugopal; Sunny Arokia Swamy;
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作者单位

Department of Electrical and Electronics Engineering, Ramaiah Institute of Technology, Bengaluru, India, 560054;

Wipro Limited, Pune, India, 411005;

Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC, USA, 28223;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Electroencephalography; Training; Epilepsy; Machine learning; Electrodes; Conferences; Informatics;

机译：脑电图;培训;癫痫;机器学习;电极;会议;信息学;;

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1. Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis [J] . XunChen, ChenHe, HuPeng Journal of applied mathematics . 2014,第1期

机译：基于集合经验模态分解和多集典范相关分析的单通道脑电信号去除人工假象
2. An Empirical Analysis of Different Machine Learning Techniques for Classification of EEG Signal to Detect Epileptic Seizure [J] . Sandeep Kumar Satapathy, Alok Kumar Jagadev, Satchidananda Dehuri Informatica: An International Journal of Computing and Informatics . 2017,第1期

机译：不同机器学习技术对脑电信号分类以检测癫痫发作的实证分析
3. An Empirical Analysis of Different Machine Learning Techniques for Classification of EEG Signal to Detect Epileptic Seizure [J] . Sandeep Kumar Satapathy, Satchidananda Dehuri, Alok Kumar Jagadev International Journal of Applied Engineering Research . 2016,第1aPta2期

机译：不同机器学习技术对脑电信号分类以检测癫痫发作的实证分析
4. Removal of muscle artifacts from EEG based on ensemble empirical mode decomposition and classification of seizure using machine learning techniques [C] . Kusumika Krori Dutta, Kavya Venugopal, Sunny Arokia Swamy International Conference on Inventive Computing and Informatics . 2017

机译：使用机器学习技术，基于集合经验模式分解和癫痫发作分类从脑电图中移除肌肉伪影
5. Design of a simulator for neonatal multichannel EEG: Application to time-frequency approaches for automatic artifact removal and seizure detection. [D] . Al-Sa'd, Mohamed Fathi. 2016

机译：新生儿多通道脑电图模拟器的设计：应用于时频方法以自动去除伪影和癫痫发作。
6. Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach [O] . Rajesh Patel, K. Gireesan, S. Sengottuvel, -1

机译：通过整合经验模式分解与回归方法相结合的EEG记录抑制心脏和眼部伪像的通用方法
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）

Removal of muscle artifacts from EEG based on ensemble empirical mode decomposition and classification of seizure using machine learning techniques

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