Diagnosis of Alzheimer’s disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features

Background: Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people.Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosi...

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Vydáno v:Frontiers in aging neuroscience Ročník 15; s. 1288295
Hlavní autoři: Zheng, Xiaowei, Wang, Bozhi, Liu, Hao, Wu, Wencan, Sun, Jiamin, Fang, Wei, Jiang, Rundong, Hu, Yajie, Jin, Cheng, Wei, Xin, Chen, Steve Shyh-Ching
Médium: Journal Article
Jazyk:angličtina
Vydáno: Lausanne Frontiers Research Foundation 07.11.2023
Frontiers Media S.A
Témata:
Alzheimer's disease
Alzheimer’s disease (AD)
Biomarkers
Classification
complexity
Decision trees
Dementia
Dementia disorders
Diagnosis
Discriminant analysis
EEG
electroencephalogram (EEG)
Electroencephalography
Entropy
Integration
Machine learning
Medical imaging
Neurodegenerative diseases
Neuroimaging
Signal processing
spectrum
supervised machine learning
Support vector machines
Synchronization
Time series
Tomography
ISSN:1663-4365, 1663-4365
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Shrnutí:Background: Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people.Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD.Methods: In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of restingstate EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation.Results: The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65%, 95.86%, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects.Conclusion: This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.
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ISSN:1663-4365
1663-4365
DOI:10.3389/fnagi.2023.1288295