Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

Chaos
Yuzhen CaoJing Liu

Abstract

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regi...Continue Reading

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Citations

Apr 4, 2017·Scientific Reports·Sung-Chun TangJiann-Shing Jeng
Jun 20, 2019·Frontiers in Neuroinformatics·Arturo Martínez-RodrigoAntonio Fernández-Caballero
May 3, 2019·Computational Intelligence and Neuroscience·Mingai LiLijuan Duan
Aug 28, 2020·Frontiers in Neuroscience·Haitao YuZhiyong Zhang
Jan 14, 2021·Journal of Sport Rehabilitation·Steven M DaviLindsey K Lepley
Sep 19, 2021·Medical & Biological Engineering & Computing·Matous CejnekIvo Bukovsky

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