Abstract
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.
Highlights
Alzheimer’s disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age (Liu et al, 2014)
As methods focused on functional neural activity, studies based on the temporal behavior of neural activity were conducted using electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (Greicius et al, 2004; Jeong, 2004; Stam, 2005; Dickerson and Sperling, 2008; Takahashi, 2013; Yang and Tsai, 2013; Wang et al, 2017; Nobukawa et al, 2020)
Both Multiscale entropy (MSE) and MF analysis showed a reduction in EEG complexity in AD patients
Summary
Alzheimer’s disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age (Liu et al, 2014). As methods focused on functional neural activity, studies based on the temporal behavior of neural activity were conducted using electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) (Greicius et al, 2004; Jeong, 2004; Stam, 2005; Dickerson and Sperling, 2008; Takahashi, 2013; Yang and Tsai, 2013; Wang et al, 2017; Nobukawa et al, 2020) Among all these evaluations, EEG is cost-effective, widely available, and non-invasive, making it ideal for clinical applications (Vecchio et al, 2013; Kulkarni and Bairagi, 2018). We performed MSE analysis and multifractal analysis on the EEGs of patients with AD and healthy controls (HC)
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