Accurate assessment of low-function autistic children based on EEG feature fusion

Autism spectrum disorder (ASD) is a very serious neurodevelopmental disorder and diagnosis mainly depends on the clinical scale, which has a certain degree of subjectivity. It is necessary to make accurate evaluation by objective indicators. In this study, we enrolled 96 children aged from 3 to 6 years: 48 low function autistic children (38 males and 10 females; mean +/- SD age: 4.9 +/- 1.1 years) and 48 typically developing (TD) children (38 males and 10 females; mean +/- SD age: 4.9 +/- 1.2 years) to participate in our experiment. We investigated to fuse multi-features (entropy, relative power, coherence and bicoherence) to distinguish low-function autistic children and TD children accurately. Minimum redundancy maximum correlation algorithm was used to choose the features and support vector machine was used for classification. Ten-fold cross validation was used to test the accuracy of the model. Better classification result was obtained. We tried to provide a reliable basis for clinical evaluation and diagnosis for ASD. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study enrolled 96 children aged 3 to 6 years and accurately distinguished low-function autistic children and typically developing children by fusing multiple features. The support vector machine was used for classification and achieved better classification results.