Characterization of Alzheimer's disease using EEG as a potential early diagnostic tool

Abstract

Alzheimer's disease (AD) is characterized by distinctive cytological, histological, and immunohistochemical changes that differentiate it from normal aging patterns. Although definitive diagnostic confirmation is anatomopathological, current diagnostic criteria combine clinical and biological evidence to formulate a probabilistic diagnosis. Given the high prevalence and socioeconomic impact of AD, there is an intensified search for early diagnostic tools. Electroencephalography (EEG) has emerged as a potential tool for early AD characterization and diagnosis, providing records of brain electrical activity that may reveal specific patterns of neuronal alteration associated with AD before clinical symptoms are evident. This study proposes using EEG to identify spectral power and complexity features to distinguish between healthy individuals and those with AD. The findings suggest that individuals with AD exhibit notable decreases in alpha (8-13 Hz) and beta (13-30 Hz) oscillations compared to healthy controls, which are associated with neuronal deterioration and loss of synaptic connectivity, hallmark characteristics of AD progression.

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