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dc.contributor.authorHelgadóttir, Halla
dc.contributor.authorGudmundsson, Ólafur Ó
dc.contributor.authorBaldursson, Gísli
dc.contributor.authorMagnússon, Páll
dc.contributor.authorBlin, Nicolas
dc.contributor.authorBrynjólfsdóttir, Berglind
dc.contributor.authorEmilsdóttir, Ásdís
dc.contributor.authorGudmundsdóttir, Gudrún B
dc.contributor.authorLorange, Málfrídur
dc.contributor.authorNewman, Paula K
dc.contributor.authorJóhannesson, Gísli H
dc.contributor.authorJohnsen, Kristinn
dc.date.accessioned2015-07-27T12:40:25Zen
dc.date.available2015-07-27T12:40:25Zen
dc.date.issued2015en
dc.date.submitted2015en
dc.identifier.citationBMJ Open 2015, 5 (1):e005500en
dc.identifier.issn2044-6055en
dc.identifier.pmid25596195en
dc.identifier.doi10.1136/bmjopen-2014-005500en
dc.identifier.urihttp://hdl.handle.net/2336/561073en
dc.descriptionTo access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.en
dc.description.abstractThe aim of this study was to develop and test, for the first time, a multivariate diagnostic classifier of attention deficit hyperactivity disorder (ADHD) based on EEG coherence measures and chronological age.
dc.description.abstractThe participants were recruited in two specialised centres and three schools in Reykjavik.
dc.description.abstractThe data are from a large cross-sectional cohort of 310 patients with ADHD and 351 controls, covering an age range from 5.8 to 14 years. ADHD was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV) criteria using the K-SADS-PL semistructured interview. Participants in the control group were reported to be free of any mental or developmental disorders by their parents and had a score of less than 1.5 SDs above the age-appropriate norm on the ADHD Rating Scale-IV. Other than moderate or severe intellectual disability, no additional exclusion criteria were applied in order that the cohort reflected the typical cross section of patients with ADHD.
dc.description.abstractDiagnostic classifiers were developed using statistical pattern recognition for the entire age range and for specific age ranges and were tested using cross-validation and by application to a separate cohort of recordings not used in the development process. The age-specific classification approach was more accurate (76% accuracy in the independent test cohort; 81% cross-validation accuracy) than the age-independent version (76%; 73%). Chronological age was found to be an important classification feature.
dc.description.abstractThe novel application of EEG-based classification methods presented here can offer significant benefit to the clinician by improving both the accuracy of initial diagnosis and ongoing monitoring of children and adolescents with ADHD. The most accurate possible diagnosis at a single point in time can be obtained by the age-specific classifiers, but the age-independent classifiers are also useful as they enable longitudinal monitoring of brain function.
dc.description.sponsorshipIcelandic Technology Development Fund 071201007 Landspitali University Hospital Research Funden
dc.language.isoenen
dc.publisherBMJ Publishing Groupen
dc.relation.urlhttp://dx.doi.org/ 10.1136/bmjopen-2014-005500en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4298102/en
dc.relation.urlhttp://bmjopen.bmj.com/content/5/1/e005500.full.pdf+htmlen
dc.rightsArchived with thanks to BMJ openen
dc.subjectADHDen
dc.subjectBörnen
dc.subjectRannsókniren
dc.subject.meshAttention Deficit Disorder with Hyperactivityen
dc.subject.meshElectroencephalographyen
dc.subject.meshCross-Sectional Studiesen
dc.subject.meshDiagnosisen
dc.subject.meshAdolescenten
dc.subject.meshChilden
dc.titleElectroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study.en
dc.typeArticleen
dc.contributor.department[ 1 ] Mentis Cura, Reykjavik, Iceland, [ 2 ] Landspitali Univ Hosp, Dept Child & Adolescent Psychiat, Reykjavik, Icelanden
dc.identifier.journalBMJ openen
dc.rights.accessOpen Accessen
html.description.abstractThe aim of this study was to develop and test, for the first time, a multivariate diagnostic classifier of attention deficit hyperactivity disorder (ADHD) based on EEG coherence measures and chronological age.
html.description.abstractThe participants were recruited in two specialised centres and three schools in Reykjavik.
html.description.abstractThe data are from a large cross-sectional cohort of 310 patients with ADHD and 351 controls, covering an age range from 5.8 to 14 years. ADHD was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV) criteria using the K-SADS-PL semistructured interview. Participants in the control group were reported to be free of any mental or developmental disorders by their parents and had a score of less than 1.5 SDs above the age-appropriate norm on the ADHD Rating Scale-IV. Other than moderate or severe intellectual disability, no additional exclusion criteria were applied in order that the cohort reflected the typical cross section of patients with ADHD.
html.description.abstractDiagnostic classifiers were developed using statistical pattern recognition for the entire age range and for specific age ranges and were tested using cross-validation and by application to a separate cohort of recordings not used in the development process. The age-specific classification approach was more accurate (76% accuracy in the independent test cohort; 81% cross-validation accuracy) than the age-independent version (76%; 73%). Chronological age was found to be an important classification feature.
html.description.abstractThe novel application of EEG-based classification methods presented here can offer significant benefit to the clinician by improving both the accuracy of initial diagnosis and ongoing monitoring of children and adolescents with ADHD. The most accurate possible diagnosis at a single point in time can be obtained by the age-specific classifiers, but the age-independent classifiers are also useful as they enable longitudinal monitoring of brain function.


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