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      • Department of Electrical and Electronics Engineering
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      Machine-based learning system: classification of ADHD and non-ADHD participants

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      Author(s)
      Öztoprak, H.
      Toycan, M.
      Alp, Y. K.
      Arıkan, Orhan
      Doğutepe, E.
      Karakaş, S.
      Date
      2017
      Source Title
      Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      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 confronted with many problems. In this paper, a novel classification approach that discriminates ADHD and non-ADHD groups over the time-frequency domain features of ERP recordings is presented. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain best discriminating features. When only three of these features were used the accuracy of classification reached to 98%, and use of six features further improved classification accuracy to 99.5%. The proposed scheme was tested with a new experimental setup and 100% accuracy is obtained. The results were obtained using RCV. The classification performance of this study suggests that TFHA can be employed as a core component of the diagnostic and prognostic procedures of various psychiatric illnesses.
      Keywords
      Attention-deficit/hyperactivity disorder (ADHD)
      Classification
      Feature selection
      Machine learning
      Support vector machine-recursive feature elimination (SVM-RFE)
      Time-frequency Hermite atomizer
      Education
      Feature extraction
      Learning systems
      Accuracy of classifications
      Classification accuracy
      Classification approach
      Classification performance
      Hermite
      Permalink
      http://hdl.handle.net/11693/37579
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2017.7960704
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      • Department of Electrical and Electronics Engineering 3868
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