Journal of Zhejiang University SCIENCE C

, Volume 15, Issue 12, pp 1147–1153 | Cite as

Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data



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 words

Self-similarity Power-law behavior Wavelet analysis Electroencephalogram Epilepsy Seizure 

CLC number

TN911.7 R318 


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Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringUbon Ratchathani UniversityUbon RatchathaniThailand

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