Examination of Multiple Spectral Exponents of Epileptic ECoG Signal

Suparerk Janjarasjitt; Kenneth A. Loparo

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Examination of Multiple Spectral Exponents of Epileptic ECoG Signal

机译：癫痫ECoG信号的多个光谱指数的检查

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In this paper, the wavelet-based fractal analysis is applied to analyze an electrocorticogram (ECoG) signal recorded from an epilepsy patient. A spectral exponent 7 yielded from the wavelet-based fractal analysis is determined from a slope of log_2 var(d_(m,n))-m graph over an interval of levels. Rather than a single spectral exponent, multiple spectral exponents determined from various intervals of levels corresponding to various ranges of spectral subbands of the ECoG signal are examined. From the computational results, it is observed that the spectral exponents of the ECoG signal estimated from different intervals of levels m exhibit different intriguing characteristics. It is also shown that the spectral exponents of the ECoG signal obtained during epileptic seizure events are significantly different from those of the ECoG signal obtained during non-seizure period. Furthermore, during non-seizure period the spectral exponent of the ECoG signal tends to decrease as the corresponding range of frequencies of subband decreases. On the contrary, for almost all spectral subbands the spectral exponents of the ECoG signal tend to be comparable except the lowest frequency subband.

机译：在本文中，基于小波的分形分析被应用于分析癫痫患者记录的脑电图（ECoG）信号。由基于小波的分形分析得出的光谱指数7是从水平间隔上的log_2 var（d_（m，n））-m图的斜率确定的。而不是单个频谱指数，而是检查从对应于ECoG信号频谱子带各个范围的电平的各个间隔确定的多个频谱指数。从计算结果可以看出，从水平m的不同间隔估计的ECoG信号的频谱指数表现出不同的吸引人的特性。还显示在癫痫发作期间获得的ECoG信号的光谱指数与在非癫痫发作期间获得的ECoG信号的光谱指数显着不同。此外，在非癫痫发作期间，随着子带频率的相应范围减小，ECoG信号的频谱指数趋于减小。相反，对于几乎所有频谱子带，除最低频率子带外，ECoG信号的频谱指数趋于可比。

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来源
《IAENG Internaitonal journal of computer science》 |2014年第3期|174-184|共11页
作者
Suparerk Janjarasjitt; Kenneth A. Loparo;
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作者单位

Department of Electrical and Electronic Engineering, Ubon Ratchathani University, Ubon Ratchathani, 34190 Thailand;

Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, 44106 USA;

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原文格式 PDF
正文语种 eng
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关键词
wavelet analysis; fractals; epilepsy; seizure; electrocorticogram;

机译：小波分析分形癫痫;发作;脑电图;

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机译：基于复杂的ECOG信号模型，用于检测大鼠诱导癫痫发作
2. A unified approach for detection of induced epileptic seizures in rats using ECoG signals [J] . NiknazarM., MousaviS.R., MotaghiS., Epilepsy & behavior: E&B . 2013,第2期

机译：使用ECoG信号检测大鼠诱发的癫痫发作的统一方法
3. A unified approach for detection of induced epileptic seizures in rats using ECoG signals [J] . NiknazarM., MousaviS.R., MotaghiS., Epilepsy & behavior: E&B . 2013,第2期

机译：使用ECoG信号检测大鼠诱发的癫痫发作的统一方法
4. Epileptic seizure detection from ECoG signals acquired with experimental epilepsy [C] . Kutlu Funda, Kose Cemal Signal Processing and Communications Applications Conference . 2013

机译：从通过实验性癫痫获得的ECoG信号中检测癫痫发作
5. On multiple-antenna communications: Signal detection, error exponent and quality of service. [D] . Li, Qiang. 2007

机译：在多天线通信上：信号检测，错误指数和服务质量。
6. Complex network based models of ECoG signals for detection of induced epileptic seizures in rats [O] . Zeynab Mohammadpoory, Mahda Nasrolahzadeh, Naghmeh Mahmoodian, 2019

机译：基于复杂的ECOG信号模型用于检测大鼠诱导癫痫发作
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

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8. Minimum-Cross-Entropy Spectral Analysis of Multiple Signals [R] . Johnson, R. W., Shore, J. E. 1981

机译：多信号的最小交叉熵谱分析

Examination of Multiple Spectral Exponents of Epileptic ECoG Signal

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