Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis

Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosi...

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Published inFrontiers in neuroscience Vol. 15; p. 667614
Main Authors Ando, Momo, Nobukawa, Sou, Kikuchi, Mitsuru, Takahashi, Tetsuya
Format Journal Article
LanguageEnglish
Published Lausanne Frontiers Research Foundation 28.06.2021
Frontiers Media S.A
Subjects
Accuracy
Alzheimer's disease
Brain
Dementia
Dementia disorders
Diagnosis
early diagnosis
EEG
EEG signal
Electrodes
Electroencephalography
Entropy
Eye movements
Fractals
Magnetic resonance imaging
multifractal
multiscale entropy
Neurodegenerative diseases
Neuroscience
Time series
Wavelet transforms
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Summary:Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are emphasized. As a diagnostic index for AD patients, evaluating the complexity of the dependence of the electroencephalography (EEG) signal on the temporal scale of Alzheimer's disease (AD) patients is effective. Multiscale entropy analysis and multifractal analysis have been performed individually, and their usefulness as diagnostic indicators has been confirmed, but the complemental relationship between these analyses, which may enhance diagnostic accuracy, has not been investigated. We hypothesize that combining multiscale entropy and fractal analyses may add another dimension to understanding the alteration of EEG dynamics in AD. In this study, we performed both multiscale entropy and multifractal analyses on EEGs from AD patients and healthy subjects. We found that the classification accuracy was improved using both techniques. These findings suggest that the use of multiscale entropy analysis and multifractal analysis may lead to the development of AD diagnostic tools.
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Edited by: Yu Chen, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), China
Reviewed by: Men-Tzung Lo, National Central University, Taiwan; Andras Eke, Semmelweis University, Hungary; Renato Anghinah, University of São Paulo, Brazil
This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2021.667614