EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions

Abstract

Alzheimer's Disease (<i>AD</i>) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect <i>AD</i> from electroencephalographic (<i>EEG</i>) recordings is evaluated. For this purpose, clinical <i>EEG</i> recordings from 14 patients with <i>AD</i> (8 with mild <i>AD</i> and 6 with moderate <i>AD</i>) and 10 healthy, age-matched individuals are analyzed. The <i>EEG</i> signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each <i>EEG</i> rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.