An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a person's cognition and behaviour. It is a lifelong condition which cannot be cured completely using any intervention to date. However, early diagnosis and follow-up treatments have a major impact on autistic people. Unfortunately, the current diagnostic practices, which are subjective and behaviour dependent, delay the diagnosis at an early age and makes it harder to distinguish autism from other developmental disorders. Several works of literature explore the possible behaviour-independent measures to diagnose ASD. Abnormalities in EEG can be used as reliable biomarkers to diagnose ASD. This work presents a low-cost and straightforward diagnostic approach to classify ASD based on EEG signal processing and learning models. Possibilities to use a minimum number of EEG channels have been explored. Statistical features are extracted from noise filtered EEG data before and after Discrete Wavelet Transform. Relevant features and EEG channels were selected using correlation-based feature selection. Several learning models and feature vectors have been studied and possibilities to use the minimum number of EEG channels have also been explored. Using Random Forest and Correlation-based Feature Selection, an accuracy level of 93% was obtained.