Investigating the Brain Development in Newborns by Information-Based Analysis of Electroencephalography (EEG) Signal

Abstract

In this paper, we employ the information theory to analyze the development of brain as the newborn ages. We compute the Shannon entropy of Electroencephalography (EEG) signal during sleep for 10 groups of newborns who are aged 36 weeks to 45 weeks (first to the last group). Based on the obtained results, EEG signals for newborns in 36 weeks have the lowest information content, whereas EEG signals for newborns in 45 weeks show the greatest information content. Therefore, we concluded that the information content of EEG signal increases as the age of newborn increases. Th result of statistical analysis demonstrated that the influence of increment of age of newborn on the variations of informant content of their EEG signals was significant.