Journal
INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY
Volume 93, Issue 1, Pages 162-166Publisher
ELSEVIER
DOI: 10.1016/j.ijpsycho.2013.01.008
Keywords
ADHD; EEG power spectra; Support vector machines; Karnaugh map
Ask authors/readers for more resources
Machine learning techniques that combine multiple classifiers are introduced for classifying adult attention deficit hyperactivity disorder (ADHD) subtypes based on power spectra of EEG measurements. The analyzed sample includes 117 adults (67 ADHD, 50 controls). The measurements are taken for four different conditions: two resting conditions (eyes open and eyes closed) and two neuropsychological tasks (visual continuous performance test and emotional continuous performance test). We divide the sample into four data sets, one for each condition. Each data set is used for training of four different support vector machine classifiers, while the output of classifiers is combined using logical expression derived from the Karnaugh map. The results show that this approach improves the discrimination between ADHD and control groups, as well as between ADHD subtypes. (C) 2013 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available