Detecting Brain activity in ADHD children and healthy controls using Machine Learning Techniques

P Natarajan, S Madanian - … of the 2024 Australasian Computer Science …, 2024 - dl.acm.org
This study focuses on Attention Deficit Hyperactivity Disorder (ADHD), a
neurodevelopmental disorder that affects both children and adults. Individuals with ADHD …

ADHD classification using auto-encoding neural network and binary hypothesis testing

Y Tang, J Sun, C Wang, Y Zhong, A Jiang, G Liu… - Artificial Intelligence in …, 2022 - Elsevier
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent
neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD …

Machine learning and adhd mental health detection-a short survey

C Nash, R Nair, SM Naqvi - 2022 25th International Conference …, 2022 - ieeexplore.ieee.org
This paper explores the current machine learning based methods used to identify Attention
Deficit Hyperactivity Disorder (ADHD) in humans. With ADHD being one of the most …

Objective ADHD diagnosis using convolutional neural networks over daily-life activity records

P Amado-Caballero… - IEEE journal of …, 2020 - ieeexplore.ieee.org
Attention Deficit/Hyperactivity Disorder (ADHD) is the most common neurobehavioral
disorder in children and adolescents. However, its etiology is still unknown, and this hinders …

A Survey of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data.

SD Silva, S Dayarathna, G Ariyarathne… - … Journal of Online & …, 2019 - search.ebscohost.com
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 …

Regional contribution in electrophysiological-based classifications of attention deficit hyperactive disorder (ADHD) using machine learning

N Chauhan, BJ Choi - Computation, 2023 - mdpi.com
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition
in children and is characterized by challenges in maintaining attention, hyperactivity, and …

Investigating current state-of-the-art applications of supportive technologies for individuals with ADHD

F Husain - arXiv preprint arXiv:2005.09993, 2020 - arxiv.org
Attention Deficit Hyperactivity Disorder (ADHD) is a chronic mental and behavioral disorder
that interferes with everyday activities and has three core symptoms: inattention …

ADHD diagnosis from multiple data sources with batch effects

E Olivetti, S Greiner, P Avesani - Frontiers in systems neuroscience, 2012 - frontiersin.org
The Attention Deficit Hyperactivity Disorder (ADHD) affects the school-age population and
has large social costs. The scientific community is still lacking a pathophysiological model of …

Wearable Motion Sensors in the Detection of ADHD: A Critical Review

J Basic, J Uusimaa, J Salmi - Nordic Conference on Digital Health and …, 2024 - Springer
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with
inattention, hyperactivity, and impulsivity as core symptoms. Current diagnostic methods of …

Task-rate-related neural dynamics using wireless EEG to assist diagnosis and intervention planning for preschoolers with ADHD exhibiting heterogeneous cognitive …

IC Chen, CL Chen, CH Chang, ZC Fan… - Journal of Personalized …, 2022 - mdpi.com
This study used a wireless EEG system to investigate neural dynamics in preschoolers with
ADHD who exhibited varying cognitive proficiency pertaining to working memory and …