Newborn sleep stage identification using multiscale entropy

Abstract

Neonatal sleep stage identification is of great importance as it helps diagnosis of certain possible disabilities in newborns. The sleep stage identification is normally done manually for an entire sleep recording which requires great human resources; therefore a reliable automated sleep stage identification system offers a helpful tool for specialists. This study demonstrated a new method for automated sleep stage scoring in neonates. The automated approach comprises two major steps: feature extraction and classification. This study presented a new approach for feature extraction based on multiscale entropy (MSE), a recently developed method for the analysis of time series and physiological signals. The features were extracted from a single EEG recording where 13 recordings from preterm infants and 14 from full term infants were used. The classification was done using the Weka software with three different classifiers: neural networks, random forests, and classification via regression. The performance of the proposed method was found to be comparable to the methods reported in the literature. The reported accuracy was found to be 0.813 for preterm subjects and 0.864 for fullterm subjects.