Performance analysis of lifting based DWT and MLPNN for epilepsy seizure from EEG

机译：基于提升的DWT和MLPNN在脑电图癫痫发作中的性能分析

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EEG recording are used to analyze the electrical signals generated by the brain. It is used in diagnosing and monitoring process of neurological disorder such as Epilepsy. Epilepsy cannot be controlled by available medical treatments. Its major manifestation is epilepsy seizure. Lifting Based Discrete Wavelet Transform (LBDWT) an efficient toll for representing electroencephalogram signals. EEG changes will be classified by Multilayer perceptron Neural Network (MLPNN). The classification rules were extracted from EEG that were reordered from healthy volunteers, epilepsy patients during seizure free interval and epilepsy patients during epileptic seizure. EEG signals were used as input of the MLPNNs trained with Back propagation and Levenberg — Marquadrant algorithm. Decision making was done in two stages: feature extraction by using LBDWT and classification using MLPNNs trained with the BP and LM algorithms. In this paper, we present an algorithm for classification of EEG (normal and Epilepsy) signals based on lifting based Discrete Wavelet Transformation and patterns recognize techniques.

机译：脑电图记录用于分析大脑产生的电信号。它用于诊断和监视神经系统疾病（例如癫痫症）的过程。癫痫不能通过现有的药物治疗来控制。其主要表现是癫痫发作。基于提升的离散小波变换（LBDWT）是表示脑电图信号的有效收费方式。脑电图的变化将通过多层感知器神经网络（MLPNN）进行分类。从EEG中提取分类规则，这些规则从健康志愿者，无癫痫发作间隔的癫痫患者和癫痫发作期间的癫痫患者重新排序。脑电信号用作通过反向传播和Levenberg-Marquadrant算法训练的MLPNN的输入。决策分两个阶段进行：使用LBDWT进行特征提取和使用经过BP和LM算法训练的MLPNN进行分类。在本文中，我们提出了一种基于提升的离散小波变换和模式识别技术的脑电信号（正常和癫痫）信号分类算法。

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《2013 International Conference on Human Computer Interactions》|2013年|1-7|共7页
会议地点 Chennai(IN)
作者
Vani S.; Suresh G.R.;
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作者单位

Vivekanandha College of Technology for Women, Tiruchengode, Tamil Nadu, India;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Artificial neural networks; Discrete wavelet transforms; Electroencephalography; Epilepsy; Feature extraction; Matrix decomposition; EEG; Epilepsy; Epileptic seizure; LBDWT; MLPNN;

机译：人工神经网络;离散小波变换；脑电图；癫痫;特征提取;矩阵分解；脑电图;癫痫;癫痫发作; LBDWT； MLPNN；;

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专利

1. Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network - Springer [J] . Yatindra Kumar, M. L. Dewal, R. S. Anand Signal, Image and Video Processing . 2014,第7期

机译：使用基于DWT的ApEn和人工神经网络在脑电图中进行癫痫发作检测-Springer
2. A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy [J] . Adeli H., Ghosh-Dastidar S., Dadmehr N. IEEE Transactions on Biomedical Engineering . 2007,第2期

机译：用于脑电图和脑电图子带分析的小波混沌方法，用于检测癫痫和癫痫
3. A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy [J] . Hojjat Adeli, Samanwoy Ghosh-Dastidar, Nahid Dadmehr IEEE Transactions on Biomedical Engineering . 2007,第期

机译：用于脑电图和脑电图子带分析的小波混沌方法，用于检测癫痫和癫痫
4. Performance analysis of lifting based DWT and MLPNN for epilepsy seizure from EEG [C] . Vani S., Suresh G.R. International Conference on Human Computer Interactions . 2013

机译：脑电图癫痫癫痫发作升降的抗颌性能分析
5. Quantitative analysis of EEG for the detection and localization of seizures in patients with medically refractory epilepsy. [D] . Battiston, John Joseph, Jr. 1999

机译：脑电图定量分析以检测和定位难治性癫痫患者的癫痫发作。
6. A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG [O] . Duo Chen, Suiren Wan, Jing Xiang, 2012

机译：基于离散小波变换和脑电图的高性能癫痫发作检测算法
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）

Performance analysis of lifting based DWT and MLPNN for epilepsy seizure from EEG

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