A Multi-modal Clinical Dataset for Critically-Ill and Premature Infant Monitoring: EEG and Videos

Abstract

The comprehensive monitoring of cardio-respiratory and neurological events of premature infants is desired for the Neonatal Intensive Care Unit (NICU). Video-based infant monitoring is an emerging tool for NICU as it eliminates skin irritations and enables new measurements like pain assessment. A multi-modal clinical dataset across the measurement of EEG and videos will be helpful in developing novel monitoring solutions for infant care. In this paper, we created such a dataset by simultaneously collecting the EEG signals and videos data from critically ill and preterm infants in NICU. Along with the recordings, we used the video-based cardio-respiratory measurements (heart rate and respiratory rate) to examine the validity of video recordings. We employed a classical video-based physiological measurement framework called Spatial Redundancy in combination with living-skin detection to measure the vital signs of recorded infants. The pilot measurements show the feasibility as well as the challenges that need to be addressed in algorithmic design in the next step. The dataset will be made publicly available to facilitate the research in this area. It will be useful for studying the video-based infant monitoring and its fusion with EEG, which may lead to new measurements such as a neonatal PSG for infant sleep staging and disease analysis (e.g. neonatal encephalopathy, neonatal respiratory distress syndrome).