High Dimensional Convolutional Neural Network for EEG Connectivity-Based Diagnosis of ADHD.

ABSTRACT:

Background

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults and its early detection is effective in the successful treatment of children. Electroencephalography (EEG) has been widely used for classifying ADHD and normal children. In recent years, deep learning leads to more accurate classification.

Objective

This study aims to adapt convolutional neural networks (CNNs) for classifying ADHD and normal children based on the connectivity measure of their EEG signals.

Material and methods

In this experimental study, the dataset consisted of 61 ADHD and 60 normal children from which 13021 epochs were extracted as input for model training and evaluation. Synchronization likelihood (SL) and wavelet coherence (WC)

SUBMITTER: Mafi M 

PROVIDER: S-EPMC9759645 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature