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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Veröffentlicht in:	Frontiers in aging neuroscience Jg. 15; S. 1288295
Hauptverfasser:	Zheng, Xiaowei, Wang, Bozhi, Liu, Hao, Wu, Wencan, Sun, Jiamin, Fang, Wei, Jiang, Rundong, Hu, Yajie, Jin, Cheng, Wei, Xin, Chen, Steve Shyh-Ching
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Lausanne Frontiers Research Foundation 07.11.2023 Frontiers Media S.A
Schlagworte:	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
Online-Zugang:	Volltext
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