Based on entropy algorithm and functional connection analysis of EEG in autism

Early diagnosis of children with autism spectrum disorder (ASD) is critical. EEG is a signal that reflects the spontaneous and rhythmic electrophysiological activity of neurons in the brain, and contains a large amount of physiological and pathological information. In this paper, two entropy features, permutation entropy and wavelet entropy, as well as two functional connectivity features, coherence and phase synchronization, were extracted from the EEG signals of ASD children and normal children, and then independent samples t-test was used to analyze the differences between groups. The results show that the entropy value of the autism group is lower than that of the normal control group, the EEG complexity is lower, and the wavelet entropy difference is the most significant; Compared with the normal group, the EEG signal connectivity of the autism group was weaker, and the four frequency bands of delta, theta, alpha and beta were significantly different, especially the difference between the frontal lobe and other brain regions was the most obvious. The results of this study can provide a reliable scientific basis for the clinical diagnosis of autism.