Analysis of electroencephalographic signals complexity regarding Alzheimer's Disease

Alzheimer's Disease (AD) is the most common type of dementia with world prevalence of more than 46 million people. The Mini-Mental State Examination (MMSE) score is used to categorize the severity and evaluate the disease progress. The electroencephalogram (EEG) is a cost-effective diagnostic tool and lately, new methods have developed for MMSE score correlation with EEG markers. In this paper, EEG recordings acquired from 14 patients with mild and moderate AD and 10 control subjects are analyzed in the five EEG rhythms (δ, θ, α, β, γ). Then, 38 linear and non-linear features are calculated. Multiregression linear analysis showed highly correlation of with MMSE score variation with Permutation Entropy of δ rhythm, Sample Entropy of θ rhythm and Relative θ power. Also, the best statistically significant regression models in terms of R2 are at O2 (0.542) and F4 (0.513) electrodes and at posterior (0.365) and left-temporal cluster (0.360).