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Title: Detection of Attention Deficit and Hyperactivity Disorder by Nonlinear EEG Analysis
Authors: Nassehi, Farhad
Sönmez, İrem
Sabanoğlu, Beril
Yukselen, Elifnur
Özaydın, Hilal Meva
Eroğul, Osman
Keywords: Attention Deficit Hyperactivity Disorder
Electroencephalography signals
Non-linear Features
Machine Learning
Issue Date: 2022
Publisher: IEEE
Abstract: This study proposes to experts a fast and highly successful algorithm for the diagnosis of ADHD disorder using EEG (Electroencephalogram) signals obtained during the Attention task, reducing their dependence on subjective evaluations. Accordingly, EEG signals obtained from 61 ADHD and 60 control participants were analyzed using nonlinear features (approximate entropy, Petrosian, and Lyapunov exponent). After feature extraction, the classification process was performed using support vector machine (SVM), and K-Nearest-Neighbor (KNN), and ensemble learning. In this study t-test based and location based feature selection methods were used. We used only features that were extracted from prefrontal and frontal regions. The highest accuracy that was reached in this study was 95.8%.
Description: Medical Technologies Congress (TIPTEKNO) -- OCT 31-NOV 02, 2022 -- Antalya, TURKEY
ISBN: 978-1-6654-5432-2
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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