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

• Published in: Journal of clinical neuroscience Vol. 90; pp. 351 - 358
• Main Authors: Kang, Jiannan, Jin, Yajuan, Liang, Guanhao, Li, Xiaoli
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2021
• Subjects: ASD; Assessment; Children; EEG; Multi-feature; ASD; Children; EEG; Multi-feature; Assessment
• ISSN: 0967-5868; 1532-2653
• DOI: 10.1016/j.jocn.2021.06.022

•Relatively large sample sizes of low-function autistic children showed differences in EEG power, entropy, coherence and bicoherence than TD children.•Multi-features were used to distinguish low-function autistic and TD children accurately.•The satisfied classification accuracy is 95.67%. 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.