Diagnostic potential of electroencephalogram analysis methods in Alzheimer’s disease: A narrative review

This review focuses on the application of electroencephalography for the early diagnosis of Alzheimer’s disease and assesses its potential as an accessible screening tool for cognitive impairment. The objective of this work is to summarize and analyze existing approaches to the application of electroencephalography methods for the diagnosis and monitoring of Alzheimer’s disease, as well as to identify key features of bioelectrical activity associated with cognitive impairment in AD compared to normal aging processes. The review examined 64 original studies (1998–2024) focused on the diagnosis of Alzheimer’s disease using electroencephalography, primarily employing spectral analysis, coherence analysis, event-related potentials, and graph theory. Data synthesis was performed using a descriptive method. Collectively, contemporary methods for analyzing electroencephalographic data demonstrate significant potential for identifying neurophysiological markers of Alzheimer’s disease. Specifically, spectral analysis reveals an increase in delta and theta rhythm power alongside a decrease in alpha activity and individual alpha frequency, reflecting neurodegenerative processes and impaired cognitive regulation. Coherence analysis indicates a disintegration of functional connections between cortical regions, manifested as reduced coherence in the alpha and beta bands and a compensatory increase in slow rhythms. Event-related potential analysis, particularly of the P300 and N400 components, points to slowed information processing and reduced efficiency of cognitive mechanisms. Graph theory methods, specifically small-world network analysis, complement this picture by demonstrating a disrupted balance between local specialization and global integration of neural networks. In turn, the application of machine learning algorithms based on these metrics opens avenues for enhancing diagnostic accuracy, predicting the progression of cognitive impairment, and developing automated systems for the early detection of Alzheimer’s disease. Quantitative electroencephalography analysis methods hold considerable promise for the early screening of Alzheimer’s disease by revealing characteristic patterns of brain activity. But for the effective utilization of EEG, the standardization of recording protocols, unification of analytical methods, and integration with machine learning algorithms are essential.

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