Automatic Classification of Sleep/Wake States of Newborns Using Deep Learning

Abstract

Premature newborns are usually admitted to the neonatal intensive care unit (NICU) for several weeks to receive advanced medical management. However, we need to investigate the NICU environment because the light and noise-emitting monitoring devices and medical equipment can adversely affect the circadian rhythm, which is the sleep-wake cycle of newborns. There are methods and devices available to measure the sleep-wake state of newborns, but they can be burdensome to newborns and nurses. Therefore, this study proposes a non-contact, automatic newborn wakefulness state classification method. The proposed method classifies sleep-wake states using 3DCNN from the entire body and the face region video. Experimental results showed that, by combining the results of the two videos, the classification performance was improved from our previous research using OpticalFlow and SVM.