Machine-based learning system: classification of ADHD and non-ADHD participants

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buir.contributor.authorArıkan, Orhan
buir.contributor.orcidArıkan, Orhan|0000-0002-3698-8888
dc.contributor.authorÖztoprak, H.en_US
dc.contributor.authorToycan, M.en_US
dc.contributor.authorAlp, Y. K.en_US
dc.contributor.authorArıkan, Orhanen_US
dc.contributor.authorDoğutepe, E.en_US
dc.contributor.authorKarakaş, S.en_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2018-04-12T11:44:30Z
dc.date.available2018-04-12T11:44:30Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 15-18 May 2017en_US
dc.descriptionConference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.description.abstractAttention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is confronted with many problems. In this paper, a novel classification approach that discriminates ADHD and non-ADHD groups over the time-frequency domain features of ERP recordings is presented. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain best discriminating features. When only three of these features were used the accuracy of classification reached to 98%, and use of six features further improved classification accuracy to 99.5%. The proposed scheme was tested with a new experimental setup and 100% accuracy is obtained. The results were obtained using RCV. The classification performance of this study suggests that TFHA can be employed as a core component of the diagnostic and prognostic procedures of various psychiatric illnesses.en_US
dc.identifier.doi10.1109/SIU.2017.7960704en_US
dc.identifier.urihttp://hdl.handle.net/11693/37579
dc.language.isoTurkishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2017.7960704en_US
dc.source.titleProceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.subjectAttention-deficit/hyperactivity disorder (ADHD)en_US
dc.subjectClassificationen_US
dc.subjectFeature selectionen_US
dc.subjectMachine learningen_US
dc.subjectSupport vector machine-recursive feature elimination (SVM-RFE)en_US
dc.subjectTime-frequency Hermite atomizeren_US
dc.subjectEducationen_US
dc.subjectFeature extractionen_US
dc.subjectLearning systemsen_US
dc.subjectAccuracy of classificationsen_US
dc.subjectClassification accuracyen_US
dc.subjectClassification approachen_US
dc.subjectClassification performanceen_US
dc.subjectHermiteen_US
dc.titleMachine-based learning system: classification of ADHD and non-ADHD participantsen_US
dc.title.alternativeMakina temelli öğrenim sistemi: DEHB olan ve olmayan katılımcıların sınıflandırmasıen_US
dc.typeConference Paperen_US

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