Document Type

Article

Publication Date

2019

DOI

10.3991/ijoe.v15i13.10744

Publication Title

International Journal of Online and Biomedical Engineering

Volume

15

Issue

13

Pages

61-76

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 behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses 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.

Comments

"This journal provides open access to all of its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Such access is associated with increased readership and increased citation of an author's work"

Published under an Attribution 3.0 Austria (CC BY 3.0 AT) license.

Original Publication Citation

De Silva, S., Dayarathna, S., Ariyarathne, G., Meedeniya, D., & Jayarathna, S. (2019). A survey of Attention Deficit Hyperactivity Disorder identification using psychophysiological data. International Journal of Online and Biomedical Engineering, 15(13), 61-76. doi:10.3991/ijoe.v15i13.10744

ORCID

0000-0002-4879-7309 (Jayarathna)

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