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Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms

Title
Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms
Authors
Abibullaev, B[Abibullaev, Berdakh]An, J[An, Jinung]
DGIST Authors
Abibullaev, B[Abibullaev, Berdakh]; An, J[An, Jinung]
Issue Date
2012-08
Citation
Journal of Medical Systems, 36(4), 2675-2688
Type
Article
Article Type
Article
Keywords
ADHDADHD DiagnosisAlgorithmAlgorithmsAttention Deficit DisorderAttention Deficit Disorder With HyperactivityChildClinical ArticleDecision Making, Computer-AssistedDecision Support SystemDiagnostic AccuracyDiagnostic ProcedureDSM-IVEEGElectroencephalogramElectroencephalographyEntropyHumanHumansMeasurementMutual InformationSchool ChildShannon TheorySignal ProcessingSVM
ISSN
0148-5598
Abstract
Attention deficit hyperactivity disorder is a complex brain disorder which is usually difficult to diagnose. As a result many literature reports about the increasing rate of misdiagnosis of ADHD disorder with other types of brain disorder. There is also a risk of normal children to be associated with ADHD if practical diagnostic criteria are not supported. To this end we propose a decision support system in diagnosing of ADHD disorder through brain electroencephalographic signals. Subjects of 10 children participated in this study, 7 of them were diagnosed with ADHD disorder and remaining 3 children are normal group. Our main goal of this sthudy is to present a supporting diagnostic tool that uses signal processing for feature selection and machine learning algorithms for diagnosis.Particularly, for a feature selection we propose information theoretic which is based on entropy and mutual information measure. We propose a maximal discrepancy criterion for selecting distinct (most distinguishing) features of two groups as well as a semi-supervised formulation for efficiently updating the training set. Further, support vector machine classifier trained and tested for identification of robust marker of EEG patterns for accurate diagnosis of ADHD group. We demonstrate that the applicability of the proposed approach provides higher accuracy in diagnostic process of ADHD disorder than the few currently available methods. © 2011 Springer Science+Business Media, LLC.
URI
http://hdl.handle.net/20.500.11750/1618
DOI
10.1007/s10916-011-9742-x
Publisher
Springer
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Collection:
ETCETC
Division of IoT∙Robotics Convergence Research1. Journal Articles
ETC1. Journal Articles


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