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

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第12期15卷 1147-1153页

作者： Suparerk JANJARASJITT Department of Electrical and Electronic Engineering Ubon Ratchathani University

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 Daubeehies wavelet bases with various numbers of vanishing moments were applied to analyze tile 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： Self-similarity power-law behavior Wavelet analysis Electroencephalogram Eoilensv Seizure

Criticality in Two-Variable Earthquake Model on a Random Graph

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Communications in Theoretical Physics 2008年第8期50卷 417-420页

作者： SUN Fan ZHANG Duan-Ming physics Laboratory Center Nanjing University of Aeronautics and Astronautics Nanjing 221000 China Department of Physics Huazhong University of Science and Technology Wuhan 430074 China

A two-variable earthquake model on a quenched random graph is established here. It can be seen as a generalization of the OFC models. We numerically study the critical behavior of the model when the system is nonconservative： the result indicates that the model exhibits self-organized criticality deep within the nonconservative regime. The probability distribution for avalanche size obeys finite size scaling. We compare our mode/with the mode/ introduced by Stefano Lise and Maya Paczuski [Phys. Rev. Lett. 88 （2002） 228301], it is proved that they are not in the same universality class.

关键词： self-organized criticality two-variable earthquake model critical behavior power-law behavior finite size scaling

Preferential attachment network model with aging and initial attractiveness

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Communications in Theoretical Physics 2022年第3期74卷 97-103页

作者： Xiao-Long Peng Complex Systems Research Center Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Center for Applied Mathematics Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi UniversityTaiyuan 030006ShanxiChina

In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of *** network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the *** our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node *** is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network *** show that depending on the aging parameter α two different network topologies can *** αbehavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/***,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/*** α>1,such scaling behavior disappears and the degree distribution is exponential.

关键词： complex network model aging preferential attachment power-law behavior

Nonlinear Dynamical behavior in BS Evolution Model Based on Small-World Network Added with Mechanism of Preferential Connection

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Nonlinear Dynamical Behavior in BS Evolution Model Based on ...