Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data
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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.
Key wordsSelf-similarity Power-law behavior Wavelet analysis Electroencephalogram Epilepsy Seizure
CLC numberTN911.7 R318
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- Andrzejak, R.G., Lehnertz, K., Mormann, F., et al., 2001. Indications of nonlinear deterministic and finitedimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E, 64:061907.1–061907.8. [doi:10.1103/PhysRevE.64.061907]CrossRefGoogle Scholar
- Janjarasjitt, S., Loparo, K.A., 2009. Wavelet-based fractal analysis of the epileptic EEG signal. Int. Symp. on Intelligent Signal Processing and Communication Systems, p.127–130. [doi:10.1109/ISPACS.2009.5383886]Google Scholar
- Janjarasjitt, S., Loparo, K.A., 2010. Wavelet-based fractal analysis of multi-channel epileptic ECoG. IEEE Region 10 Conf. TENCON, p.373–378. [doi:10.1109/TENCON.2010.5686662]Google Scholar
- Janjarasjitt, S., Loparo, K.A., 2014b. Scale-invariant behavior of epileptic ECoG. J. Med. Biol. Eng., in press. [doi:10.5405/jmbe.1433]Google Scholar
- Pachori, R.B., 2008. Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res. Lett. Signal Process., 2008:293056.1–293056.5. [doi:10.1155/2008/293056]Google Scholar
- Watters, P.A., 1998. Fractal Structure in the Electroencephalogram. Available from http://www.complexity.org.au/ci/vol05/watters/watters.html Google Scholar
- Wornell, G.W., 1991. Synthesis, Analysis, and Processing of Fractal Signals. PhD Thesis, Massachusetts Institute of Technology, Massachusetts, USA.Google Scholar
- Wornell, G.W., 1995. Signal Processing with Fractals: a Wavelet-Based Approach. Prentice Hall, New Jersey, USA.Google Scholar