Identification of Alzheimer’s disease brain networks based on EEG phase synchronization

• Published in: Biomedical engineering online Vol. 24; no. 1; pp. 32 - 21
• Main Authors: Cao, Jiayi, Li, Bin, Li, Xiaoou
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 09.03.2025; BioMed Central; BMC
• Subjects: Accuracy; Aged; Alzheimer Disease - diagnosis; Alzheimer Disease - physiopathology; Alzheimer's disease; Artificial neural networks; Brain; Brain - physiopathology; Brain mapping; Brain research; Case-Control Studies; Classification; Cognitive ability; Cognitive Dysfunction - physiopathology; Cognitive impairment; Connectivity analysis; Data acquisition; Deep learning; Diagnosis; EEG; Electroencephalogram; Electroencephalography; Electroencephalography Phase Synchronization; Eye movements; Female; Functional connectivity; Graph neural networks; Humans; Learning algorithms; Machine learning; Male; Methods; Middle Aged; Nerve Net - physiopathology; Network analysis; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neurosciences; Occipital lobes; Patients; Phase lag; Signal Processing, Computer-Assisted; Support vector machines; Synchronism; Synchronization; China; Functional connectivity; Alzheimer’s disease; Electroencephalogram; Cognitive impairment
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-025-01361-0

Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer's disease (AD) and to assist in the early diagnosis of Alzheime...