Machine learning approach for classification of ADHD adults.
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.
Faculty for Computer Science and Engineering, University of Skopje, Former Yugoslav Republic of Macedonia. Electronic address: aleksandar.tenev@finki.ukim.mk.
Aleksandar
A
Tenev
Macedonian Academy of Sciences and Arts, Skopje, Former Yugoslav Republic of Macedonia.
Silvana
S
Markovska-Simoska
Macedonian Academy of Sciences and Arts, Skopje, Former Yugoslav Republic of Macedonia.
Ljupco
L
Kocarev
Macedonian Academy of Sciences and Arts, Skopje, Former Yugoslav Republic of Macedonia.
Jordan
J
Pop-Jordanov
Brain and Trauma Foundation Grison/Switzerland, Poststrasse 22, 7000 Chur, Switzerland.
Andreas
A
Müller
Brain and Trauma Foundation Grison/Switzerland, Poststrasse 22, 7000 Chur, Switzerland.
Gian
G
Candrian
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001289:Attention Deficit Disorder with Hyperactivity / Q000145:classification* / Q000523:psychology; D039721:Diagnostic and Statistical Manual of Mental Disorders; D004569:Electroencephalography / Q000145:classification; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D011597:Psychomotor Performance / Q000502:physiology; D060388:Support Vector Machine; D055815:Young Adult
ADHD; EEG power spectra; Karnaugh map; Support vector machines