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 diagnosis of...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 10 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173767681&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173767681 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2023 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 173767681 173767681 173767681 10.3389/fnagi.2023.1288295 173767681 ppf: 1 ppct: 9 formats: tig: atl: Diagnosis of Alzheimer’s disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features. aug: au: Xiaowei Zheng Bozhi Wang Hao Liu Wencan Wu Jiamin Sun Wei Fang Rundong Jiang Yajie Hu Cheng Jin Xin Wei Steve Shyh-Ching Chen affil: Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China. sug: subj: Alzheimer's Disease Diagnosis Electroencephalography Methods Brain Physiology Reproducibility of Results Biological Markers Human Algorithms Support Vector Machine Descriptive Statistics Random Forest Middle Age Aged Male Female Factor Analysis Psychological Tests Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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 resting-state 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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