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Abstract

Background:Nonlinear measures like Sample Entropy which is a measure of chaos based on self-similarity extended from dynamic systems & chaos theory can potentially help to interpret time-series data obtained from EEG signals and facilitate understanding of complex profiles seen in ASD. The aim of this study is to characterize EEG patterns in ASD cohorts by extracting non-linear measures (Sample Entropy) from time-frequency data in awake and sleep stages.Methods:Unmedicated Preschool children, ages 2 to 6 years, underwent EEG recording up to 180 mins with 64 Channel EGI-GES400 system after a sleep deprivation protocol. ASD group had 28 children (24M, 4F). The controls were those with ADHD, Specific Speech Delay, and Global Developmental Delay had 10 children (6M, 4F). Awake and sleep stage 1 (N1), 2 (N2) and 3 (N3) recordings were included. Artifact free continuous segment of 60s were selected manually. Each 10s epoch was decomposed into 6 bands (gamma high through delta) by wavelets. Sample Entropy (SE) was computed for each band per channel. Mean Differences were computed between groups in awake & sleep stages. Significance was estimated with p-values (α=0.05).Results and conclusions:Differences in entropies was significant (p < 0.05) in N2 & N3. In higher bands (gamma & beta), ASD was dominant in parietal & frontal particularly Pz. In N3,non-ASD had dominant delta in occipital, parietal & frontal regions.A higher entropy translates to more possible states of being. Delta band is more dynamic in non-ASD group while higher bands are more dynamic in ASD group.