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

Fatma LATİFOĞLU, (Erciyes Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü, Kayseri, Türkiye)
Mustafa Yasin ESAS, (Erciyes Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü, Kayseri, Türkiye)
Ramis İLERİ, (Erciyes Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü, Kayseri, Türkiye)
Sevgi ÖZMEN, (Erciyes Üniversitesi, Tıp Fakültesi, Çocuk ve Ergen Ruh Sağlığı ve Hastalıkları Anabilim Dalı, Kayseri, Türkiye)
Esra DEMİRCİ (Erciyes Üniversitesi, Tıp Fakültesi, Çocuk ve Ergen Ruh Sağlığı ve Hastalıkları Anabilim Dalı, Kayseri, Türkiye)
Turkish Journal of Medical Sciences
8 1
Yıl: 2022 Cilt: 52 Sayı: 5 Sayfa Aralığı: 1616 - 1626 Metin Dili: İngilizce DOI: 10.55730/1300-0144.5502 İndeks Tarihi: 27-12-2022

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

Öz:
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.
Anahtar Kelime: Signal processing electrooculography visual stimulus attention deficit hyperactivity disorder classification

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA Latifoğlu F, ESAS M, ILERI R, ÖZMEN s, Demirci E (2022). Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. , 1616 - 1626. 10.55730/1300-0144.5502
Chicago Latifoğlu Fatma,ESAS Mustafa Yasin,ILERI Ramis,ÖZMEN sevgi,Demirci Esra Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. (2022): 1616 - 1626. 10.55730/1300-0144.5502
MLA Latifoğlu Fatma,ESAS Mustafa Yasin,ILERI Ramis,ÖZMEN sevgi,Demirci Esra Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. , 2022, ss.1616 - 1626. 10.55730/1300-0144.5502
AMA Latifoğlu F,ESAS M,ILERI R,ÖZMEN s,Demirci E Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. . 2022; 1616 - 1626. 10.55730/1300-0144.5502
Vancouver Latifoğlu F,ESAS M,ILERI R,ÖZMEN s,Demirci E Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. . 2022; 1616 - 1626. 10.55730/1300-0144.5502
IEEE Latifoğlu F,ESAS M,ILERI R,ÖZMEN s,Demirci E "Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms." , ss.1616 - 1626, 2022. 10.55730/1300-0144.5502
ISNAD Latifoğlu, Fatma vd. "Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms". (2022), 1616-1626. https://doi.org/10.55730/1300-0144.5502
APA Latifoğlu F, ESAS M, ILERI R, ÖZMEN s, Demirci E (2022). Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. Turkish Journal of Medical Sciences, 52(5), 1616 - 1626. 10.55730/1300-0144.5502
Chicago Latifoğlu Fatma,ESAS Mustafa Yasin,ILERI Ramis,ÖZMEN sevgi,Demirci Esra Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. Turkish Journal of Medical Sciences 52, no.5 (2022): 1616 - 1626. 10.55730/1300-0144.5502
MLA Latifoğlu Fatma,ESAS Mustafa Yasin,ILERI Ramis,ÖZMEN sevgi,Demirci Esra Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. Turkish Journal of Medical Sciences, vol.52, no.5, 2022, ss.1616 - 1626. 10.55730/1300-0144.5502
AMA Latifoğlu F,ESAS M,ILERI R,ÖZMEN s,Demirci E Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. Turkish Journal of Medical Sciences. 2022; 52(5): 1616 - 1626. 10.55730/1300-0144.5502
Vancouver Latifoğlu F,ESAS M,ILERI R,ÖZMEN s,Demirci E Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms. Turkish Journal of Medical Sciences. 2022; 52(5): 1616 - 1626. 10.55730/1300-0144.5502
IEEE Latifoğlu F,ESAS M,ILERI R,ÖZMEN s,Demirci E "Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms." Turkish Journal of Medical Sciences, 52, ss.1616 - 1626, 2022. 10.55730/1300-0144.5502
ISNAD Latifoğlu, Fatma vd. "Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms". Turkish Journal of Medical Sciences 52/5 (2022), 1616-1626. https://doi.org/10.55730/1300-0144.5502
Sayfa Oluşturulma Tarihi
24-04-2024 5:4

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