A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning.

ABSTRACT:

Objective

Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most widespread neurodevelopmental disorders diagnosed in childhood. ADHD is diagnosed by following the guidelines of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). According to DSM-5, ADHD has not yet identified a specific cause, and thus researchers continue to investigate this field. Therefore, the primary objective of this work is to present a study to find the subset of channels or brain regions that best classify ADHD vs Typically Developing children by means of Electroencephalograms (EEG).

Methods

To achieve this goal, we present a novel approach to identify the brain regions that best classify ADHD using EEG and Deep Learning (DL). First, we perform a filtering and

SUBMITTER: Sanchis J 

PROVIDER: S-EPMC10877365 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature