DETECTION OF AUTISM SPECTRUM DISORDER BY FEATURE EXTRACTION OF EEG SIGNALS AND MACHINE LEARNING CLASSIFIERS

Abstract

Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, impacts the subject’s social communication and interaction and the subjects exhibit restricted and repetitive behaviors. Subjects with ASD may need assistance throughout their life, depending on the severity. Early diagnosis of ASD is therefore critical for early intervention. ASD is diagnosed clinically based on behavioral assessments of the subjects, which results in delayed diagnosis, since the typical ASD traits due to aberrant brain development take time to develop. Neurological disorders associated with aberrant brain electrical activity have been detected by analyzing Electroencephalogram (EEG) signal patterns. In this study, we used features extracted from EEG brain waves to categorize ASD and normal subjects using Machine Learning (ML) classifiers. Autoregressive (AR) coefficients, Shannon entropy, Multifractal wavelet leader estimates, Multiscale wavelet variance and Discrete Fourier Transform (DFT) coefficients were extracted from EEG brain waves of ASD and normal subjects. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k-Nearest Neighbor (k-NN) and Feed-forward Neural Network (FNN) were utilized as classification algorithms to categorize the ASD subjects and the control subjects. An accuracy of 90% was achieved by k-NN algorithm using AR features, Shannon entropy, Multifractal wavelet leader estimates and Multiscale wavelet variance estimates in ASD categorization. An accuracy of 93% was achieved by k-NN using the DFT features. The findings of this study indicate that features extracted from EEG are sufficient enough for categorization of ASD subjects and the control subjects.