The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data

Abstract

ObjectiveTo identify autistic children, we used features extracted from two modalities (EEG and eye-tracking) as input to a machine learning approach (SVM). MethodsA total of 97 children aged from 3 to 6 were enrolled in the present study. After resting-state EEG data recording, the children performed eye-tracking tests individually on own-race and other-race stranger faces stimuli. Power spectrum analysis was used for EEG analysis and areas of interest (AOI) were selected for face gaze analysis of eye-tracking data. The minimum redundancy maximum relevance (MRMR) feature selection method combined with SVM classifiers were used for classification of autistic versus typically developing children. ResultsResults showed that classification accuracy from combining two types of data reached a maximum of 85.44%, with AUC = 0.93, when 32 features were selected. LimitationsThe sample consisted of children aged from 3 to 6, and no younger patients were included. ConclusionsOur machine learning approach, combining EEG and eye-tracking data, may be a useful tool for the identification of children with ASD, and may help for diagnostic processes.