Computer Aided Diagnosis of ASD based on EEG using RELIEFF and Supervised Learning Algorithm

Abstract

Autism Spectrum Disorder is diagnosed by physical examination of electroencephalography (EEG) signals that is very responsive to time consuming and bias. Diagnosing autism in existing research experiences low power and unsuitability for processing extensive datasets. An automated diagnosing is an essential assist to medical professionals to eliminate the problems mentioned above. In this article, a novel technique is propounded to diagnose autism from VMD, RELIEFF and supervised learning algorithms. A universal EEG dataset is adopted to explore the proposed method’s performance. The technique starts with the extraction of features from EEG signals via VMD, and to recognize the best features RELIEFF is employed. Then, to distinguish typical and autism signals, supervised learning (KNN, SVM, and ANN) methods is employed. The outcome illustrates that the proposed technique attains high accuracy, indicating a powerful way to diagnose and categorize autism.