Accurate assessment of low-function autistic children based on EEG feature fusion.
J Clin Neurosci
; 90: 351-358, 2021 Aug.
Article
en En
| MEDLINE
| ID: mdl-34275574
ABSTRACT
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.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Trastorno Autístico
/
Electroencefalografía
Tipo de estudio:
Prognostic_studies
Límite:
Child
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Child, preschool
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Female
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Humans
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Male
Idioma:
En
Revista:
J Clin Neurosci
Asunto de la revista:
NEUROLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
China