Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

Abstract

Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among widespread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-andregional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods

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

“…The subjects of this study were 16 patients with AD and 18 sexmatched and aged-matched healthy old individuals (see Table 1) (Mizuno et al, 2010;Nobukawa et al, 2019Nobukawa et al, , 2020. The sample size of AD and HC groups was determined based on previous works on complexity analysis (Abásolo et al, 2008;Mizuno et al, 2010;Nobukawa et al, 2019Nobukawa et al, , 2020. For this study, we defined healthy old individuals as nonsmokers and not on medication.…”

“…Positron emission tomography (PET) and magnetic resonance imaging (MRI) are often used to diagnose AD and detect neurotransmitter activity disorders, amyloid beta plaque deposition, and brain atrophy (Ewers et al, 2011;McKhann et al, 2011;Sperling et al, 2011). 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).…”

“…Moreover, EEG dynamics at each temporal scale and frequency band, such as theta, beta, and gamma bands, are associated with different memory function components, cognitive and perceptual function (Klimesch et al, 2007). Hence, as a diagnostic index for AD patients with various brain function defects, the evaluation of the complexity with temporal scale dependence in EEG signal is effective (Mizuno et al, 2010;Nobukawa et al, 2019Nobukawa et al, , 2020.…”

Phenotyping Neuropsychiatric Symptoms Profiles of Alzheimer’s Disease Using Cluster Analysis on EEG Power

“…Although previous studies repeatedly demonstrated that patients with Alzheimer's disease show an increase in delta/theta band power, a decline in alpha/beta band power, reduced complexity and impaired synchrony in EEG signals compared to non-Alzheimer individuals (Dauwels et al, 2010;Nobukawa et al, 2019Nobukawa et al, , 2020Smailovic and Jelic, 2019), there have been limited data on assessing the EEG signals associated with neuropsychiatric conditions for Alzheimer's patients with the same severity of dementia. While neuropsychiatric symptoms generally increase as the stage of dementia progresses, many symptoms still develop in the early stage of Alzheimer's disease (Panza et al, 2010;Lyketsos et al, 2011;Hashimoto et al, 2015).…”

“…Given that the behavioral and psychological symptoms vary to an extent among Alzheimer’s patients even though if they have the same severity of dementia, understanding the variation should help initiate preventive measures, organize appropriate care, and facilitate applicable treatment plans for these patients. While previous studies had examined the difference in EEG signals of Alzheimer’s patients vs. non-Alzheimer’s patients recognizing alternation of EEG power, and/or reduction of temporal complexity and functional connectivity in Alzheimer’s disease (Dauwels et al, 2010 ; Nobukawa et al, 2019 , 2020 ; Smailovic and Jelic, 2019 ), this study aimed to explore the potential difference in EEG scans of heterogeneous Alzheimer’spatients. Our hypothesis was that between patients with the same severity of disease, there would still be a difference in their neuropsychiatric symptoms with a corresponding difference in their EEG signals.…”