Investigation of Hybrid Feature Selection Techniques for Autism Classification using EEG Signals

Autism Spectrum Disorder (ASD), the non-uniform
neurodevelopment condition that is characterized by the
impairment of behaviour in communication and social
interaction with some restricted their repetitive behaviour.
Today, to measure the voltage created during brain activity is
measured using electroencephalography (EEG). The wavelet
transform is used for decomposing the time-frequency of the
EEG signal. Feature Selection is the process that significantly
reduces feature space dimensionality, while maintaining the right
representation of their original data. In this work, metaheuristic
algorithm is utilized for feature selection. The proposed feature
selection is based on River Formation Dynamics (RFD) and a
hybrid Greedy RFD is presented. Support Vector Machine
(SVM) can be a concept consisting of a set of methods of
supervised learning to analyze pattern recognition that is a
successful tool in the analysis of regression and classification.
Experimental results show the proposed Greedy RFD feature
selection improves the performance of the classifiers and enhance
the accuracy of classifying ASD.