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.
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Acknowledgements
We thank our colleagues who provided assistance in data acquisition sessions. Especially, we appreciate the efforts of Dr. Yunhee Shin and her research group during the research collaboration (Daegu Univ., ROK) and their fruitful discussions about the ADHD children. We are also immensely grateful to reviewers for their useful comments on an earlier version of themanuscript. This work was supported by the Daegu Gyeongbuk Institute of Science and Technology and funded by the Ministry of Education, Science and Technology of the Republic of Korea.
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Abibullaev, B., An, J. Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms. J Med Syst 36, 2675–2688 (2012). https://doi.org/10.1007/s10916-011-9742-x
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DOI: https://doi.org/10.1007/s10916-011-9742-x