ADHD Diagnosis and Biomarker Detection Based on Multimodal Graph Convolutional Neural Network

Y Gao, X Wang, A Jiang, Y Chen… - ICASSP 2024-2024 …, 2024 - ieeexplore.ieee.org
In this study, we apply a graph convolutional network (GCN) in attention deficit hyperactivity
disorder (ADHD) classification by using multimodal data. Here, multimodal data is integrated …

Identification of attention deficit hyperactivity disorder with deep learning model

Ö Kasim - Physical and Engineering Sciences in Medicine, 2023 - Springer
This article explores the detection of Attention Deficit Hyperactivity Disorder, a
neurobehavioral disorder, from electroencephalography signals. Due to the unstable …

Integrating multimodal MRIs for adult ADHD identification with heterogeneous graph attention convolutional network

D Yao, E Yang, L Sun, J Sui, M Liu - Predictive Intelligence in Medicine …, 2021 - Springer
Adult attention-deficit/hyperactivity disorder (ADHD) is a mental health disorder whose
symptoms would change over time. Compared with subjective clinical diagnosis, objective …

Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks

Y Chang, C Stevenson, IC Chen… - Journal of Neural …, 2022 - iopscience.iop.org
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that
pervasively interferes with the lives of individuals starting in childhood. Objective. To …

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD

JD Vera, R Freichel, G Michelini… - Journal of Attention …, 2024 - journals.sagepub.com
Objective: ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of
inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are …

Trends, limits, and challenges of computer technologies in attention deficit hyperactivity disorder diagnosis and treatment

R Montaleão Brum Alves… - … , Behavior, and Social …, 2022 - liebertpub.com
Attention deficit hyperactivity disorder (ADHD) is a neurobiological condition that appears
during an individual's childhood and may follow her/him for life. The research objective was …

Determination of the Optimal EEG-based Features to Detect ADHD by Machine Learning Algorithms

Y Can, İ Bigat, F Nassehi, A Eken… - 2023 Medical …, 2023 - ieeexplore.ieee.org
This study proposes a highly accurate and fast algorithm for the diagnosis of attention deficit
hyperactivity disorder (ADHD), which will reduce reliance on time-consuming subjective …

Hexa-Net framework: A fresh ADHD-specific model for identifying ADHD based on integrating brain atlases

DA Al-Ubaidi, AA Samah, M Jasim - International Conference on …, 2023 - Springer
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a frequent neurodevelopmental
disorder affecting children and adults, which is routinely diagnosed based on subjective …

[HTML][HTML] Tmp19: a novel ternary motif pattern-based adhd detection model using eeg signals

PD Barua, S Dogan, M Baygin, T Tuncer, EE Palmer… - Diagnostics, 2022 - mdpi.com
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition
worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset …

[HTML][HTML] Measuring attention of ADHD patients by means of a computer game featuring biometrical data gathering

MA Teruel, J Sanchis, N Ruiz-Robledillo… - Heliyon, 2024 - cell.com
ADHD is a neurodevelopmental disorder diagnosed mainly in children, marked by
inattention and hyperactivity-impulsivity. The symptoms are highly variable, such as different …