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.
