基于小波分析的癫痫脑电图自相似性测量

Suparerk JANJARASJITT

doi:10.1631/jzus.C1400126

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基于小波分析的癫痫脑电图自相似性测量

电子工程 | Updated：2022-05-19

- 基于小波分析的癫痫脑电图自相似性测量
- Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data
- 信息与电子工程前沿（英文） 2014年15卷第12期页码：1147-1153
- Affiliations：
  
  Department of Electrical and Electronic Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
- Author bio：
  
  [ "Suparerk JANJARASJITT, E-mail: suparerk.j@ubu.ac.th" ]
- Funds：
- DOI：10.1631/jzus.C1400126
  中图分类号： TN911.7;R318
- 收稿：2014-04-05，
  
  修回：2014-11-18，
  
  纸质出版：2014-12
- Accepted：
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基于小波分析的癫痫脑电图自相似性测量[J]. 信息与电子工程前沿（英文）, 2014,15(12):1147-1153.

Suparerk JANJARASJITT. Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(12): 1147-1153.
基于小波分析的癫痫脑电图自相似性测量[J]. 信息与电子工程前沿（英文）, 2014,15(12):1147-1153. DOI： 10.1631/jzus.C1400126.

Suparerk JANJARASJITT. Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(12): 1147-1153. DOI： 10.1631/jzus.C1400126.

摘要

研究目的

自相似性或尺度不变性是信号（包括脑电图信号）中的一个重要特征。本文基于小波变换介绍一种估计谱指数的计算方法。

创新要点

提出1/

过程基于小波分析的表征，验证其能有效估计用于表征自相似性的谱指数。

研究方法

引入频域分析中的1/

过程，介绍其基于小波分析的表征，提出基于小波分析的自相似性测量基本步骤。通过数据分析验证所提方法的正确性（图3-6）。

重要结论

计算结果表明基于小波分析的1/

过程能够有效估计表征自相似性的谱指数。小波变换方法适用于自相似或尺度不变的信号。基于小波分析估计得到的颅内脑电图信号谱指数与功率谱方法估计得到的数据相差无几。癫痫惊厥时较之非惊厥时段获取的颅内脑电图信号具有更高的自相似性。

Abstract

Self-similarity or scale-invariance is a fascinating characteristic found in various signals including electroencephalogram (EEG) signals. A common measure used for characterizing self-similarity or scale-invariance is the spectral exponent. In this study

a computational method for estimating the spectral exponent based on wavelet transform was examined. A series of Daubechies wavelet bases with various numbers of vanishing moments were applied to analyze the self-similar characteristics of intracranial EEG data corresponding to different pathological states of the brain

i.e.

ictal and interictal states

in patients with epilepsy. The computational results show that the spectral exponents of intracranial EEG signals obtained during epileptic seizure activity tend to be higher than those obtained during non-seizure periods. This suggests that the intracranial EEG signals obtained during epileptic seizure activity tend to be more self-similar than those obtained during non-seizure periods. The computational results obtained using the wavelet-based approach were validated by comparison with results obtained using the power spectrum method.

关键词

Keywords

references

P. Abry , , , P. Goncalves , , , P. Flandrin . . Wavelet-based spectral analysis of 1/ f processes . . IEEE Int. Conf. on Acoustics, Speech, and Signal Processing , , 1993 . . p.237 - - 240 . . DOI: 10.1109/ICASSP.1993.319479 http://doi.org/10.1109/ICASSP.1993.319479 . .

R.G. Andrzejak , , , K. Lehnertz , , , F. Mormann , , , 等 . . Indications of nonlinear deterministic and finitedimensional structures in time series of brain electrical activity: dependence on recording region and brain state . . Phys. Rev. E , , 2001 . . 64 061907.1 - - 061907.8 . . DOI: 10.1103/PhysRevE.64.061907 http://doi.org/10.1103/PhysRevE.64.061907 . .

A.L. Goldberger . . Complex systems . . Proc. Am. Thorac. Soc. , , 2006 . . 3 467 - - 471 . . DOI: 10.1513/pats.200603-028MS http://doi.org/10.1513/pats.200603-028MS . .

A.L. Goldberger , , , L.A.N. Amaral , , , J.M. Hausdorff , , , 等 . . Fractal dynamics in physiology: alterations with disease and aging . . PNAS , , 2002 . . 99 ( ( suppl 1 ): ): 2466 - - 2472 . . DOI: 10.1073/pnas.012579499 http://doi.org/10.1073/pnas.012579499 . .

S. Janjarasjitt . . Computational validation of fractal characterization by using the wavelet-based fractal analysis . . J. Korean Phys. Soc. , , 2014 . . 64 ( ( 6 ): ): 780 - - 785 . . DOI: 10.3938/jkps.64.780 http://doi.org/10.3938/jkps.64.780 . .

S. Janjarasjitt , , , K.A. Loparo . . Wavelet-based fractal analysis of the epileptic EEG signal . . Int. Symp. on Intelligent Signal Processing and Communication Systems , , 2009 . . p.127 - - 130 . . DOI: 10.1109/ISPACS.2009.5383886 http://doi.org/10.1109/ISPACS.2009.5383886 . .

S. Janjarasjitt , , , K.A. Loparo . . Wavelet-based fractal analysis of multi-channel epileptic ECoG . . IEEE Region 10 Conf. TENCON , , 2010 . . p.373 - - 378 . . DOI: 10.1109/TENCON.2010.5686662 http://doi.org/10.1109/TENCON.2010.5686662 . .

S. Janjarasjitt , , , K.A. Loparo . . Comparison of complexity measures using two complex system analysis methods applied to the epileptic ECoG . . J. Korean Phys. Soc. , , 2013 . . 63 ( ( 8 ): ): 1659 - - 1665 . . DOI: 10.3938/jkps.63.1659 http://doi.org/10.3938/jkps.63.1659 . .

S. Janjarasjitt , , , K.A. Loparo . . Examination of scaleinvariant characteristics of epileptic electroencephalograms using wavelet-based analysis . . Comput. Electr. Eng. , , 2014a . . 40 ( ( 5 ): ): 1766 - - 1773 . . DOI: 10.1016/j.compeleceng.2014.04.005 http://doi.org/10.1016/j.compeleceng.2014.04.005 . .

S. Janjarasjitt , , , K.A. Loparo . . Scale-invariant behavior of epileptic ECoG . . J. Med. Biol. Eng., in press. , , 2014b . . DOI: 10.5405/jmbe.1433 http://doi.org/10.5405/jmbe.1433 . .

V. Joshi , , , R.B. Pachori , , , A. Vijesh . . Classification of ictal and seizure-free EEG signals using fractional linear prediction . . Biomed. Signal Process. Contr. , , 2014 . . 9 1 - - 5 . . DOI: 10.1016/j.bspc.2013.08.006 http://doi.org/10.1016/j.bspc.2013.08.006 . .

J.W. Kantelhardt , , , S.A. Zschiegner , , , E. Koscielny-Bunde , , , 等 . . Multifractal detrended fluctuation analysis of nonstationary time series . . Phys. A , , 2002 . . 316 ( ( 1-4 ): ): 87 - - 114 . . DOI: 10.1016/S0378-4371(02)01383-3]CrossRefMATH http://doi.org/10.1016/S0378-4371(02)01383-3]CrossRefMATH . .

R. Lopes , , , N. Betrouni . . Fractal and multifractal analysis: a review . . Med. Image Anal. , , 2009 . . 13 ( ( 4 ): ): 634 - - 649 . . DOI: 10.1016/j.media.2009.05.003 http://doi.org/10.1016/j.media.2009.05.003 . .

B.B. Mandelbrot . . The Fractal Geometry of Nature . . W.H. Freeman and Company, San Francisco, USA.MATH , , 1982 . . .

B.B. Mandelbrot , , , J.W. van Ness . . Fractional Brownian motions, fractional noises and applications . . SIAM Rev. , , 1968 . . 10 ( ( 4 ): ): 422 - - 437 . . DOI: 10.1137/1010093 http://doi.org/10.1137/1010093 . .

R.B. Pachori . . Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition . . Res. Lett. Signal Process. , , 2008 . . 2008 293056.1 - - 293056.5 . . DOI: 10.1155/2008/293056 http://doi.org/10.1155/2008/293056 . .

R.B. Pachori , , , S. Patidar . . Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions . . Comput. Method. Progr. Biomed. , , 2014 . . 113 ( ( 2 ): ): 494 - - 502 . . DOI: 10.1016/j.cmpb.2013.11.014 http://doi.org/10.1016/j.cmpb.2013.11.014 . .

S.F. Timashev , , , Y.S. Polyakov . . Review of flicker noise spectroscopy in electrochemistry . . Fluct. Noise Lett. , , 2007 . . 7 ( ( 2 ): ): R15 - - R47 . . DOI: 10.1142/S0219477507003829 http://doi.org/10.1142/S0219477507003829 . .

S.F. Timashev , , , Y.S. Polyakov , , , R.M. Yulmetyev , , , 等 . . Analysis of biomedical signals by flicker-noise spectroscopy: identification of photosensitive epilepsy using magnetoencephalograms . . Laser Phys. , , 2009 . . 19 ( ( 4 ): ): 836 - - 854 . . DOI: 10.1134/S1054660X09040434 http://doi.org/10.1134/S1054660X09040434 . .

S.F. Timashev , , , Y.S. Polyakov , , , P.I. Misurkin , , , 等 . . Anomalous diffusion as a stochastic component in the dynamics of complex processes . . Phys. Rev. E , , 2010 . . 81 041128.1 - - 041128.17 . . DOI: 10.1103/PhysRevE.81.041128 http://doi.org/10.1103/PhysRevE.81.041128 . .

S.F. Timashev , , , O.Y. Panischev , , , Y.S. Polyakov , , , 等 . . Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia . . Phys. A , , 2012 . . 391 ( ( 4 ): ): 1179 - - 1194 . . DOI: 10.1016/j.physa.2011.09.032 http://doi.org/10.1016/j.physa.2011.09.032 . .

P.A. Watters . . Fractal Structure in the Electroencephalogram . . Available from , , 1998 . . http://www.complexity.org.au/ci/vol05/watters/watters.html http://www.complexity.org.au/ci/vol05/watters/watters.html , , . .

G.W. Wornell . . Synthesis, Analysis, and Processing of Fractal Signals . . PhD Thesis, Massachusetts Institute of Technology, Massachusetts, USA. , , 1991 . . .

G.W. Wornell . . Wavelet-based representations for the 1 /f family of fractal processes . . Proc. IEEE , , 1993 . . 81 ( ( 10 ): ): 1428 - - 1450 . . DOI: 10.1109/5.241506 http://doi.org/10.1109/5.241506 . .

G.W. Wornell . . Signal Processing with Fractals: a Wavelet-Based Approach . . Prentice Hall, New Jersey, USA. , , 1995 . . .

G.W. Wornell , , , A.V. Oppenheim . . Estimation of fractal signals from noisy measurements using wavelets . . IEEE Trans. Signal Process. , , 1992 . . 40 ( ( 3 ): ): 611 - - 623 . . . .

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