Accurate identification of ADHD among adults using real-time activity data
A Kaur, KS Kahlon - Brain sciences, 2022 - mdpi.com
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder that affects
millions of children and typically persists into adulthood. It must be diagnosed efficiently and …
millions of children and typically persists into adulthood. It must be diagnosed efficiently and …
Hyperparameter Tuning of a Deep Learning EEG-based Neural Network for the Diagnosis of ADHD
J Sanchis, MA Teruel, J Trujillo - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
In order to implement an automatic-computer-aided system for the diagnosis of the Attention-
Deficit Hyperactivity Disorder (ADHD), a Deep Learning Multihead Convolutional Based …
Deficit Hyperactivity Disorder (ADHD), a Deep Learning Multihead Convolutional Based …
Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD
H Hart, K Chantiluke, AI Cubillo, AB Smith… - Human Brain …, 2014 - Wiley Online Library
Abstract The diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) is based on
subjective measures despite evidence for multisystemic structural and functional deficits …
subjective measures despite evidence for multisystemic structural and functional deficits …
[HTML][HTML] Functional imaging derived ADHD biotypes based on deep clustering may guide personalized medication therapy
Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental
disorder with childhood onset, however, there is no clear correspondence established …
disorder with childhood onset, however, there is no clear correspondence established …
A novel application for the efficient and accessible diagnosis of ADHD using machine learning
Attention-deficit/hyperactivity disorder is the most pervasive neurodevelopmental disorder
among children and adolescents. Current clinical diagnosis, however, is inaccurate …
among children and adolescents. Current clinical diagnosis, however, is inaccurate …
Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG
Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental
disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis …
disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis …
A deep learning framework for identifying children with ADHD using an EEG-based brain network
H Chen, Y Song, X Li - Neurocomputing, 2019 - Elsevier
The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm.
However, the application of DL techniques in attention-deficit/hyperactivity disorder (ADHD) …
However, the application of DL techniques in attention-deficit/hyperactivity disorder (ADHD) …
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 …
neurological disorders among children, that affects different areas in the brain that allows …
Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
E Ghasemi, M Ebrahimi, E Ebrahimie - Cognitive Neurodynamics, 2022 - Springer
Abstract Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a
significant challenge. Misdiagnosis has significant negative medical side effects. Due to the …
significant challenge. Misdiagnosis has significant negative medical side effects. Due to the …
Towards EEG-based objective ADHD diagnosis support using convolutional neural networks
S Stock, J Hausberg, A Armengol-Urpi… - … IEEE Conference on …, 2023 - ieeexplore.ieee.org
Attention Deficit Hyperactivity Disorder (ADHD) represents a widely prevalent
neurodivergence. Current diagnostic approaches rely on subjective symptom assessment …
neurodivergence. Current diagnostic approaches rely on subjective symptom assessment …