RT Journal Article
SR Electronic
T1 Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study
JF BMJ Open
JO BMJ Open
FD British Medical Journal Publishing Group
SP e005500
DO 10.1136/bmjopen-2014-005500
VO 5
IS 1
A1 Halla Helgadóttir
A1 Ólafur Ó Gudmundsson
A1 Gísli Baldursson
A1 Páll Magnússon
A1 Nicolas Blin
A1 Berglind Brynjólfsdóttir
A1 Ásdís Emilsdóttir
A1 Gudrún B Gudmundsdóttir
A1 Málfrídur Lorange
A1 Paula K Newman
A1 Gísli H Jóhannesson
A1 Kristinn Johnsen
YR 2015
UL http://bmjopen.bmj.com/content/5/1/e005500.abstract
AB Objectives The 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.Setting The participants were recruited in two specialised centres and three schools in Reykjavik.Participants The 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.Results Diagnostic 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.Conclusions The 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.