Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms


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LATİFOĞLU F., Esas M. Y., Ileri R., ÖZMEN S., DEMİRCİ E.

Turkish Journal of Medical Sciences, vol.52, no.5, pp.1616-1626, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.55730/1300-0144.5502
  • Journal Name: Turkish Journal of Medical Sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, EMBASE, MEDLINE, Veterinary Science Database, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1616-1626
  • Keywords: attention deficit hyperactivity disorder, classification, electrooculography, Signal processing, visual stimulus
  • Kayseri University Affiliated: No

Abstract

© TÜBİTAK.Background/aim: Attention deficit hyperactivity disorder (ADHD), one of the most common neurodevelopmental disorders in childhood, is diagnosed clinically by assessing the symptoms of inattention, hyperactivity, and impulsivity. Also, there are limited objective assessment tools to support the diagnosis. Thus, in this study, a new electrooculography (EOG) based on visual stimulus tracking to support the diagnosis of ADHD was proposed. Materials and methods: Reference stimulus one-to-one tracking numbers (RSOT) and colour game detection (CGD) were applied to 53 medication-free children with ADHD and 36 healthy controls (HCs). Also, the test was applied six months after the treatment to children with ADHD. Parameters obtained during the visual stimulus tracking test were analyzed and Higuchi fractal dimension (HFD) and Hjorth parameters were calculated for all EOG records. Results: The average test success rate was higher in HCs than in children with ADHD. Based on machine learning algorithms, the proposed system can distinguish drug-free ADHD patients from HCs with an 89.13% classification performance and also distinguish drug-free children from treated children with an 80.47% classification performance. Conclusion: The findings showed that the proposed system could be helpful to support the diagnosis of ADHD and the follow-up of the treatment.