A systematic literature review of neuroimaging coupled with machine learning approaches for diagnosis of attention deficit hyperactivity disorder

I Ashraf, S Jung, S Hur, Y Park - Journal of Big Data, 2024 - Springer
Problem Attention deficit hyperactivity disorder (ADHD) is the most commonly found
neurodevelopmental condition among children with an estimated 2.5% to 9% global …

Wavelet transform, reconstructed phase space, and deep learning neural networks for EEG-based schizophrenia detection

A Al Fahoum - International journal of neural systems, 2024 - pubmed.ncbi.nlm.nih.gov
This study proposes an innovative expert system that uses exclusively EEG signals to
diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological …

[HTML][HTML] Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy

S García-Ponsoda, A Maté, J Trujillo - Computers in Biology and Medicine, 2024 - Elsevier
Background: EEG signals are commonly used in ADHD diagnosis, but they are often
affected by noise and artifacts. Effective preprocessing and segmentation methods can …

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value

E Ahmadi Moghadam, F Abedinzadeh Torghabeh… - Neuroinformatics, 2024 - Springer
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral
disorder affecting children and adolescents, requiring early detection for effective treatment …

Transforming the screening of neurodevelopmental disorders in young children

A Hervé, C Beaujard, B Bedi… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
This publication deals with an AI solution for screening developmental disorders in young
children for conditions such as autism, ADHD, learning disabilities, epilepsy, intellectual …