Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

Journal: Behavioural brain research

Published Date: Apr 19, 2019

Abstract

BACKGROUND: Previous automated EEG-based diagnosis of autism spectrum disorders (ASD) using various nonlinear EEG analysis methods were limited to distinguish only children with ASD from those normally developed without approaching their autistic features severity.

Authors

Hikmat Hadoush

Department of Rehabilitation Sciences, Faculty of Applied Medical Sciences at Jordan University of Science and Technology, Jordan. Electronic address: hmhadoush@just.edu.jo.
Maha Alafeef

Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; Department of Biomedical Engineering, Jordan University of Science and Technology, Jordan. Electronic address: alafeef2@illinois.edu.
Enas Abdulhay

Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan. ewabdulhay@just.edu.jo.

Keywords

Autism Spectrum Disorder Autistic Disorder Behavior Child Child, Preschool Electroencephalography Female Humans Male Neural Networks, Computer Sensitivity and Specificity Signal Processing, Computer-Assisted

External Resources

View on PubMed Access via DOI PubMed (30641159)

Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

Abstract

Authors

Keywords

External Resources

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