Detection of Attention Deficit Hyperactivity Disorder using Decision-level Fusion of Brain Connectivity Information Based on Deep Learning

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological disorder that often first appears in children. The condition is treated via behavioral studies, but there is no definitive technique to identify it. Electroencephalography (EEG) signals of ADHD patients are widely investigated to understand alterations in the brain. We offered EEG connectivity featured image creation to be used as input to CNN architectures in the proposed study. EEG data from 15 ADHD patients and 18 control participants are evaluated, and ADHD detection performance is shown, to demonstrate the efficacy of the suggested method. EEG connectivity featured images are obtained using six different connectivity features (magnitude square coherence, cross-power spectral density, correlation coefficient, covariance, cohentropy coefficient, and correntrophy coefficient). Deep features are extracted for each image subset using EEG connectivity-featured images to train ResNet-50. The effectiveness of the suggested strategy for detecting ADHD is assessed using the DT, LR, and SVM classifiers and decision-level fusion. According to experimental results, employing EEG connectivity featured images as input to ResNet-50 architecture, and decision-level fusion offers important advantages in identifying ADHD.

I. Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common behavioral neurodevelopmental disease that affects children and adolescents and may have long-term effects [1], [2]. Inattention, hyperactivity, and impulsivity are the three main symptoms of ADHD, which can present alongside other psychiatric disorders including depression or bipolar disorder. These symptoms frequently result in learning difficulties and moral issues, and they have a significant impact on people’s mental health, academic performance, and social connections [3]. Attentional bio-markers for ADHD cannot be found by behavioral testing. The absence of diagnostic markers for the illness makes the diagnosis uncertain. Brain signaling tests and a diagnosis based on numerical data are both employed to solve this difficulty [3].