Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity

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buir.contributor.authorArıkan, Orhan
buir.contributor.orcidArıkan, Orhan|0000-0002-3698-8888
dc.citation.epage2410en_US
dc.citation.issueNumber12en_US
dc.citation.spage2400en_US
dc.citation.volumeNumber128en_US
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.date.accessioned2018-04-12T11:12:54Z
dc.date.available2018-04-12T11:12:54Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractObjective Attention-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 still confronted with many problems. Method A novel classification approach that discriminates ADHD and nonADHD groups over the time-frequency domain features of event-related potential (ERP) recordings that are taken during Stroop task is presented. Time-Frequency Hermite-Atomizer (TFHA) technique is used for the extraction of high resolution time-frequency domain features that are highly localized in time-frequency domain. Based on an extensive investigation, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain the best discriminating features. Results When the best three features were used, the classification accuracy for the training dataset reached 98%, and the use of five features further improved the accuracy to 99.5%. The accuracy was 100% for the testing dataset. Based on extensive experiments, the delta band emerged as the most contributing frequency band and statistical parameters emerged as the most contributing feature group. Conclusion The classification performance of this study suggests that TFHA can be employed as an auxiliary component of the diagnostic and prognostic procedures for ADHD. Significance The features obtained in this study can potentially contribute to the neuroelectrical understanding and clinical diagnosis of ADHD.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:12:54Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.identifier.doi10.1016/j.clinph.2017.09.105en_US
dc.identifier.issn1388-2457
dc.identifier.urihttp://hdl.handle.net/11693/37418
dc.language.isoEnglishen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.clinph.2017.09.105en_US
dc.source.titleClinical Neurophysiologyen_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.titleMachine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activityen_US
dc.typeArticleen_US

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