| Abstrakt: |
A severe neurodevelopmental disease called autism spectrum disorder (ASD) can start as early as birth or within the first 2.5 years of life. Deficits in communication and social abilities like imitation, empathy, and shared attention, as well as constrained interests and repetitive behavioral patterns, are common in autistic children. Weak functional connections between distant brain regions and excessive functional connectivity within local cortical regions may both play a role in ASD. The activity of the human brain exhibits complex oscillations in both the spatial and temporal domains because it is a complicated nonlinear system. This study uses multiscale entropy to examine changes in brain complexity associated with ASD using EEG data. By measuring the entropy across various time scales using a coarse-graining technique, multiscale entropy (MSE) measures the complexity of physiological signals. Although the MSE model has been used in investigations of children with ASD, those studies were only able to differentiate between children with ASD and those who were normally developed without a corresponding severity level of their autistic traits. The following scalp sites of the 10-20 system were used to record EEGs from the Emotiv Epoc+: AF3, AF4, F3, F4, F7, F8, FC5, FC6, T7, T8, P7, P8, O1, and O2. Seven children with ASD and five typically developing youngsters (controls), ranging in age from 10 to 12 years, made up the research subjects. The individual was lying comfortably with their eyes closed and opened for five minutes during the EEG recording. The multiscale entropy scale is used to determine the degree of complexity in the EEG recording data. To determine if there were statistically significant differences in the complexity evaluations between the two groups of subjects, the ANNOVA statistical test was used. Based on the results of the analysis, it was found that there were significant differences in MSE values between ASD and normal subjects. The level of brain complexity in children with autism tends to be lower than in normal children. A promising biomarker for the early identification of ASD risk and problems in cognitive development may be MSE computed from resting-state EEG recordings. [ABSTRACT FROM AUTHOR] |
