title: Improving Alzheimer's disease diagnosis with machine learning techniques.
abstract: There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.
authors.affiliation: Mathematics, Computing and Cognition Center (CMCC), Universidade Federal do ABC (UFABC), São Paulo, Brazil.
authors.forename: Lucas R
authors.initials: LR
authors.lastname: Trambaiolli
authors.forename: Ana C
authors.initials: AC
authors.lastname: Lorena
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Fraga
authors.forename: Paulo A M
authors.initials: PA
authors.lastname: Kanda
authors.forename: Renato
authors.initials: R
authors.lastname: Anghinah
authors.forename: Ricardo
authors.initials: R
authors.lastname: Nitrini
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged