EEG wavelet packet power spectrum tool for checking Alzheimer's disease progression

Abstract

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases and it is strongly associated with age. There are four AD stages: mild cognitive impairment (MCI), mild, moderate (ADM) and advanced (ADA). This work aims at developing a new tool capable of distinguishing the different stages of AD at scalp level. Features such as the conventional frequencies relative power of the power spectrum wavelet packet transform have been extracted from the electroencephalogram signals in order to feed four classifiers: random forest decision trees, linear and quadratic support-vector-machines and linear discriminant analysis. The obtained results were analyzed through topographic maps and enabled the distinguish between binary groups with the following overall accuracies: 85.5% (C-MCI); 88.2% (C-ADM); 91.4% (C-ADA); 89.7% (MCI-ADM); 82.4% (MCI-ADA) and 81.3% (ADM-ADA). The applied method was able to detect major differences in scalp areas above the frontal and temporal lobes of the brain as AD progresses.