| dc.description.abstract | A fetus in the third trimester sleeps the majority of the day, which is important for the
development of the brain. When born preterm, sleep is likely to be interrupted due to
exposure to external stimuli, which could be problematic for development. This study
proposes a video-based pipeline to allow for continuous sleep monitoring of preterm infants
admitted to the NICU. Such a pipeline would allow for timing of non-essential
care, and consequently optimize sleep.
Since an infant in the NICU is typically largely occluded, we use only eye cues to classify
sleep, as typically at least one eye is visible. The proposed pipeline consists of three
main components: an eye localization model, a REM detection model, and a sleep state
classification step. The localization model successfully localizes eyes of preterm infants,
though it occasionally struggles with closed eyes. Additionally, it is capable of distinguishing
between open and closed eyes. The REM model shows promising results, but
its performance across train runs varies due to the sensitivity of the training process,
likely caused by the limited available data.
Ultimately, our results show that sleep state predictions made with our pipeline closely
align with ground truth sleep states, indicating potential for practical use. Since eye
cues alone are not always reliable for accurately predicting sleep states, we suggest a
combination of eye, face, and body cues to ultimately classify sleep more robustly. | |
