title: Evaluation of feature extraction techniques on event-related potentials for detection of attention-deficit/hyperactivity disorder.
abstract: Event-related potentials (ERPs) are one of the most informative and dynamic methods of monitoring cognitive processes, which are widely used in clinical research to deal a variety of psychiatric and neurological disorders as attention-deficit/hyperactivity disorder (ADHD). This work proposes an extraction and selection methodology for discriminating between normal and pathological patients with ADHD by using ERPs. Three different sets of features (morphological, wavelets, and nonlinear based) are analyzed, looking for the best classification accuracy. The results show that the wavelet features provided a good discriminative capability, but it improved by combining all the set of features and applying a feature selection algorithm, reaching a maximum accuracy rate of 91.3%.
authors.affiliation: Universidad Nacional de Colombia, sede Manizales, Colombia. pacastroc@unal.edu.co
authors.forename: P
authors.initials: P
authors.lastname: Castro-Cabrera
authors.forename: J
authors.initials: J
authors.lastname: Gomez-Garcia
authors.forename: F
authors.initials: F
authors.lastname: Restrepo
authors.forename: O
authors.initials: O
authors.lastname: Moscoso
authors.forename: G
authors.initials: G
authors.lastname: Castellanos-Dominguez
mesh_terms: D000293:Adolescent; D001185:Artificial Intelligence; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis* / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D002648:Child; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity