Detection of Autism Spectrum Disorders from EEG Signals using Multi-Input One-Dimensional Convolutional Neural Networks

Abstract

Autism is a neurodevelopmental disorder that typically begins in childhood and continues throughout life. Autism Spectrum Disorder (ASD) exhibits prominent features such as difficulties in social interaction, communication problems, repetitive behaviors, and restricted interests. Diagnosis is usually made using methods like clinical observation, developmental screening tools, psychological assessments, and behavioral tests, although methods like Electroencephalography (EEG) may also be used in rare cases. Artificial intelligence (AI) is increasingly playing a significant role in the diagnosis and evaluation of ASD. In the literature, there are many advanced methods that have the potential to be used for ASD diagnosis using EEG and artificial intelligence. In this study, a novel approach for EEG-based ASD detection was developed using a multi-input dimensional Convolutional Neural Network (CNN) model. The developed method was tested on an openly accessible dataset obtained from King Abdulaziz University Hospital, achieving an accuracy of 92.93%.