Study of EEG with Epileptic Activity Using Spectral Analysis and Wavelet Transform

RADU MATEI; DANIELA MATEI

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Study of EEG with Epileptic Activity Using Spectral Analysis and Wavelet Transform

机译：使用光谱分析和小波变换研究脑癫痫活动的研究

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In this paper we apply some signal processing methods to detect and classify specific patterns present in EEG signal, which give information about the inset of brain disorders, in particular epileptic activity. We analyze EEG signals using spectral analysis methods, namely Short-Time Fourier Transform and Discrete Wavelet Transform, applied to several sets of EEG recordings. The spectrograms and wavelet decompositions and spectra are shown for a few EEG sequences with typical pathological patterns, to prove the possibility of classification based on EEG spectrum.

机译：在本文中，我们应用一些信号处理方法来检测和分类EEG信号中存在的特定模式，其提供有关脑疾病的插图，特别是癫痫活动的信息。我们使用频谱分析方法分析EEG信号，即短时傅里叶变换和离散小波变换，应用于几组EEG录像。谱图和小波分解和光谱显示为具有典型病理模式的少数EEG序列，以证明基于EEG谱进行分类的可能性。

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来源
《WSEAS Transactions on Signal Processing》 |2017年第2期|共7页
作者
RADU MATEI; DANIELA MATEI;
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作者单位

Technical University "Gh.Asachi" Faculty of Electronics Telecommunications and Information Technology;

University of Medicine and Pharmacy "Gr.T.Popa" Faculty of Medical Bioengineering;

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原文格式 PDF
正文语种 eng
中图分类声学;
关键词
EEG analysis; Epileptic activity; Wavelet transform; Spectrogram;

机译：EEG分析;癫痫活动;小波变换;谱图;

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专利

1. Study of EEG with Epileptic Activity Using Spectral Analysis and Wavelet Transform [J] . RADU MATEI, DANIELA MATEI WSEAS Transactions on Signal Processing . 2017,第Pta2期

机译：使用光谱分析和小波变换研究脑癫痫活动的研究
2. Epileptic seizure detection in eeg signals using multifractal analysis and wavelet transform [J] . Uthayakumar R., Easwaramoorthy D. Fractals: An interdisciplinary journal on the complex geometry of nature . 2013,第2期

机译：使用多重分形分析和小波变换的脑电信号癫痫发作检测
3. Analysis of EEG records in an epileptic patient using wavelet transform. [J] . Adeli H, Zhou Z, Dadmehr N Journal of Neuroscience Methods . 2003,第1期

机译：使用小波变换分析癫痫患者的脑电图记录。
4. Adaptive separation of background activity and transient phenomenon in epileptic EEG using mathematical morphology and wavelet transforms [C] . Lin-Sen Pon, Mingui Sun Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE . 2000

机译：利用数学形态学和小波变换自适应分离癫痫脑电图的背景活动和瞬态现象
5. Diagnosis of Autism with EEG Analysis Using Discrete Wavelet Transform [D] . Kshirsagar, Prachi 2017

机译：基于离散小波变换的脑电图诊断自闭症
6. EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms [O] . Itaf Ben Slimen, Larbi Boubchir, Zouhair Mbarki, 2020

机译：基于双树复小波变换和机器学习算法的脑电图癫痫发作检测与分类
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）

Study of EEG with Epileptic Activity Using Spectral Analysis and Wavelet Transform

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