Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals. (March 2023)

• Record Type: Journal Article
• Authors: Karabiber Cura, Ozlem; Kocaaslan Atli, Sibel; Akan, Aydin
• Is Part Of: Biomedical signal processing and control. Volume 81(2023)
• Publication Date: 2023-03
• Subjects: Electroencephalography (EEG) -- Attention Deficit Hyperactivity Disorder (ADHD) -- Intrinsic Time-Scale Decomposition (ITD) -- Machine learning -- Connectivity features; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104512
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Attention deficit hyperactivity disorder (ADHD), a neuro-developmental condition, is characterized by various degrees of impulsivity, hyperactivity, and inattention. Treatment of this condition and minimizing its negative impact on learning, working, forming relationships, and quality of life depends heavily on the early identification. The Electroencephalography (EEG) is a useful neuroimaging technique for understanding ADHD. This study examines the brain activity of children with ADHD by analyzing the EEG signals using the intrinsic time-scale decomposition (ITD). Different combinations of the modes, known as Proper Rotation Components (PRCs), produced by ITD, are used to extract a variety of connectivity-based features (magnitude square coherence, cross power spectral density, correlation coefficient, covariance, cohentropy coefficient, correntropy coefficient). EEG signals of 15 ADHD children and 18 age-matched health children are recorded while resting with the eyes closed. Mentioned features are calculated using different channel pairs chosen from longitudinal and transversal planes. Through various machine learning approaches and a 10-fold cross-validation method, the proposed approach is evaluated to distinguish between ADHD patients and healthy controls. Classification accuracies are obtained for the longitudinal and transverse planes, between 92.90% to 99.90% and 91.70% to 100.00%, respectively. Our results support the remarkable performance of the proposed approach, and represent a substantial advance over similar studies in terms of recognizing and classifying ADHD. Highlights: Classification of ADHD EEG signals using ITD and connectivity-based feature extraction. Modes produced by ITD are used to compute connectivity features. Combinations of modes in longitudinal and transversal planes are used for feature extraction. Highest accuracies for longitudinal and transverse planes are 99.90% and 100.00%, respectively. Results represent a significant improvement over previous studies.