Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques.

Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease in the community draws an increasing graph from the past to the present. While the ADHD diagnosis is basically made with the psychiatric tests, there is no active clinically used objective diagnostic tool.

Using electrooculography with visual stimulus tracking test in diagnosing of ADHD: findings from machine learning algorithms.

Background: Attention deficit hyperactivity disorder (ADHD), one of the most common neurodevelopmental disorders in childhood, is diagnosed clinically by assessing the symptoms of inattention, hyperactivity, and impulsivity. Also, there are limited objective assessment tools to support the diagnosis. Thus, in this study, a new electrooculography (EOG) based on visual stimulus tracking to support the diagnosis of ADHD was proposed.

Diagnosis of attention-deficit hyperactivity disorder using EOG signals: a new approach.

A system based on objective data was developed in the diagnosis and follow-up of attention-deficit hyperactivity disorder (ADHD) in this study. First of all, an electronic circuit, with a two-channel instrumentation amplifier designed to detect eye movements in the horizontal and vertical directions via surface electrodes, was developed to obtain the electrooculogram (EOG) signals. In order to provide a controlled analysis of eye movements during the reception of the signal, an attention test with visual stimulus software was developed.