Improvement of EMD for Processing Nonlinear Signals

机译：用于处理非线性信号的EMD的改进

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摘要

In this paper, a new signal decomposition method called Local Mean Decomposition (LMD) and a new eliminating end swings method called Wave Form Matching (WFM) are introduced. It can get better data mean and time-frequency distribution than EMD method for analysis a classic nonlinear data.

机译：在本文中，引入了一种新的信号分解方法，称为局部平均分解（LMD）和一种称为波形匹配（WFM）的新的消除结束旋转方法。它可以获得比用于分析经典非线性数据的EMD方法更好的数据平均值和时频分布。

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来源
《International Conference on Electronic Measurement Instruments》|2005年||共4页
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作者
Gai Qiang; Wang Xiaotong; Xu Xiaogang; Zhang Haiyong; Zhou Aijun;
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原文格式 PDF
正文语种
中图分类电气测量技术及仪器;
关键词
nonlinear; time-frequency analysis; EMD method;

机译：非线性;时频分析;EMD方法;

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专利

1. A hybrid algorithm based on EEMD and EMD for multi-mode signal processing [J] . Jeng-Wen Lin Structural Engineering and Mechanics . 2011,第6期

机译：基于EEMD和EMD的混合算法用于多模式信号处理
2. MS-EMD based Signal Processing Method for Reduction of Motion Artifacts from PPG Signals [J] . M. Raghuram, Kosaraju Sivani, K. Ashoka Reddy International Journal of Applied Engineering Research . 2018,第21aPta1期

机译：基于MS-EMD的信号处理方法，用于减少PPG信号的运动伪影
3. Research on the Method of EEMD in Pulse Wave Signals Processing [J] . XU Pan-pan, CHEN Chang-jun, LOU Hai-fang 中国生物医学工程学报（英文版） . 2018,第003期

机译：EEMD方法在脉波信号处理中的研究
4. Improvement of EMD for Processing Nonlinear Signals [C] . . 2005

机译：改进EMD处理非线性信号
5. Improvement of Prony -based least square method of system identification via nonlinear parameter transformation and finite -difference processing of signals. [D] . Wu, Chih-Hao. 2000

机译：通过信号的非线性参数变换和有限差分处理，改进了基于Prony的最小二乘系统识别方法。
6. Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing [O] . Xingxing Zhang, Chao Xu, Wanli Xue, 2018

机译：基于多通道生理信号的非线性综合情感识别
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）

Improvement of EMD for Processing Nonlinear Signals

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