Machine Learning Supervised Classification Methodology for Autism Spectrum Disorder Based on Resting-State Electroencephalography (EEG) Signals
C. Bhaskarachary, Amir Jahanian Najafabadi, Benjamin Godde
Abstract
Autism Spectrum Disorder is a neurological and developmental disorder that starts early in adolescence and lasts throughout a person's life affecting information flow in the brain leading to secondary problems for the patient [1],[2]. Current diagnostic approaches for autism are time-consuming and are mainly based on clinical interviews, to accelerate this process of diagnosing the disease as early as possible with fewer efforts and better accuracy machine learning methods have been proposed recently [3],[4]. Early detection of ASD is vital in enhancing the efficiency of the treatment [5]. The motivation behind this study is the absence of well-defined automated diagnostic procedures for ASD. The objective of this study is to explore and analyze the techniques for EEG pre-processing, feature extraction, classification and identify the abnormal activity for the diagnosis of ASD based on the power spectral density of EEG signals applying machine learning models.