Comparative Study of Detection of ADHD using EEG Signals

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental irregularity which is complex, universal and heterogeneous. Inattention, hyperactivity and impulsiveness are some of the symptoms of ADHD. The disease is developing at preschool years and can even extend to adulthood when proper diagnosis is not provided. Hence detection of ADHD is very essential. ADHD detection can be done using EEG signal. In this review, we analysed the available research on deep and machine learning studies on diagnosing ADHD and found the various diagnostic setups that have been employed. The paper discusses the existing techniques present using different classifiers. It briefly explains the different methods when using Artificial Neural Network (ANN), Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) as classifier. Comparative study on these methods were done and performance measures was increased over time.