A Survey of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviors and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper discusses the existing literature on the identification of ADHD using eye movement data and fMRI together including different deep learning techniques, existing models and a thorough analysis of the existing literature. We have identified the current challenges and possible future directions to provide computational support for early identification of ADHD patients that enable early treatments.