Hikmat Hadoush1, Maha Alafeef2, Enas Abdulhay3. 1. Department of Rehabilitation Sciences, Faculty of Applied Medical Sciences at Jordan University of Science and Technology, Jordan. Electronic address: hmhadoush@just.edu.jo. 2. 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. 3. Department of Biomedical Engineering, Jordan University of Science and Technology, Jordan. Electronic address: ewabdulhay@just.edu.jo.
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. OBJECTIVES: Identifying potential differences between children with mild and sever ASD based on EEG analysis using empirical mode decomposition (EMD) and second-order difference plot (SODP) models, and determining the accuracy of such model outcome measures to distinguish ASD severity levels. METHODS: Resting-state EEG data recorded for 36 children, who divided equally into two matched groups of mild and sever ASD. EMD analysis was applied to their EEG data to identify intrinsic mode functions (IMFs) features, SODP patterns, elliptical area and central tendency measure (CTM) values. Artificial neural network then used to determine the accuracy of this models outcome measures in distinguishing between the two ASD groups. RESULTS: Children with sever ASD showed smaller, less twitches and oscillation of IMFs features, more stochastic SODP plotting, less CTM values, and higher ellipse area values compared to the children with mild ASD, which indicates their greater EEG variabilities and their greater inability to suppress their improper behavior. ANN ended with model sensitivity and specificity of 100% and 94.7%, respectively, and 97.2% overall accuracy of distinguishing between ASD groups. CONCLUSION: Children with sever and mild ASD had different IMFs features, SODP plotting, elliptical area and CTM values. In addition, these EMD outcome measures could serve as a sensitive automated tool to distinguish different severity levels in children with ASD.
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. OBJECTIVES: Identifying potential differences between children with mild and sever ASD based on EEG analysis using empirical mode decomposition (EMD) and second-order difference plot (SODP) models, and determining the accuracy of such model outcome measures to distinguish ASD severity levels. METHODS: Resting-state EEG data recorded for 36 children, who divided equally into two matched groups of mild and sever ASD. EMD analysis was applied to their EEG data to identify intrinsic mode functions (IMFs) features, SODP patterns, elliptical area and central tendency measure (CTM) values. Artificial neural network then used to determine the accuracy of this models outcome measures in distinguishing between the two ASD groups. RESULTS:Children with sever ASD showed smaller, less twitches and oscillation of IMFs features, more stochastic SODP plotting, less CTM values, and higher ellipse area values compared to the children with mild ASD, which indicates their greater EEG variabilities and their greater inability to suppress their improper behavior. ANN ended with model sensitivity and specificity of 100% and 94.7%, respectively, and 97.2% overall accuracy of distinguishing between ASD groups. CONCLUSION:Children with sever and mild ASD had different IMFs features, SODP plotting, elliptical area and CTM values. In addition, these EMD outcome measures could serve as a sensitive automated tool to distinguish different severity levels in children with ASD.
Authors: The-Hanh Pham; Jahmunah Vicnesh; Joel Koh En Wei; Shu Lih Oh; N Arunkumar; Enas W Abdulhay; Edward J Ciaccio; U Rajendra Acharya Journal: Int J Environ Res Public Health Date: 2020-02-04 Impact factor: 3.390