Detection of Attention Deficit Hyperactivity Disorder Using EEG Signals and Douglas-Peucker Algorithm

• Published in: Medical Technologies National Congress (Online) pp. 1 - 4
• Main Authors: Cura, Ozlem Karabiber, Aydin, Gamze N., Celen, Sibel, Atli, Sibel Kocaaslan, Akan, Aydin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 31.10.2022
• Subjects: ADHD; Behavioral sciences; Classification algorithms; Douglas-Peucker Algorithm; EEG; Electroencephalography; Feature extraction; Fractals; Machine learning; Machine learning algorithms; Process control
• ISSN: 2687-7783
• DOI: 10.1109/TIPTEKNO56568.2022.9960193

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological disease that typically appears in childhood. The disease has three main symptoms in children: inattention, hyperactivity, and impulsivity. Treatment of the disease is based on behavioral studies; however, there is no definitive diagnosis method. Hence, the electroencephalography (EEG) signals of ADHD subjects are often investigated to understand changes in the brain. In the proposed study, it is aimed to process and reduce the EEG data of ADHD and control subjects (CS) by using the Douglas-Peucker algorithm and to investigate the effects of the algorithm on EEG signal analysis. EEG data obtained from 18 control subjects (4 boys, 14 girls, mean age 13) and 15 ADHD patients (7 boys, 8 girls, mean age 12) are collected. By using reduced EEG data; time features such as energy, skewness, kurtosis, mean absolute deviation (MAD), root mean square (RMS), peak to peak (PTP) value, Hjorth parameters, and non-linear features such as largest Lyapunov Exponent (LLE), correlation dimension (CD), Hurst exponent (HE), Katz fractal dimension (KFD), Higuchi fractal dimension (HFD), are calculated to examine different signal characteristics. Extracted features are used to distinguish the EEG data of ADHD and CS by using various machine learning algorithms.