title: Classification of ADHD and non-ADHD using AR models.
abstract: Accurate and confident diagnosis of ADHD is highly dependent on subjective observations. Several quantitative methods have been proposed, posing it as a two-class classification problem (ADHD and non-ADHD). However, the results have not made it past the research stage yet, as misclassification rates must be close to 0%. This study aims to discriminate ADHD and non-ADHD subjects using autoregressive models, with a high level of accuracy (85-95%). In addition, a confidence metric is proposed, expressing with how much confidence the classification of ADHD and non-ADHD subjects is made.
authors.forename: Juan L
authors.initials: JL
authors.lastname: Lopez Marcano
authors.forename: Martha Ann
authors.initials: MA
authors.lastname: Bell
authors.forename: A A Louis
authors.initials: AA
authors.lastname: Beex
mesh_terms: D000465:Algorithms; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis*; D002648:Child; D016001:Confidence Intervals; D006801:Humans