Machine Vision for Patient Monitoring in the Neonatal Intensive Care Unit

Abstract

Continuous patient monitoring of newborns in the neonatal intensive care unit (NICU) is often performed with wired sensors which can be cumbersome, can interfere with parental bonding, and can irritate the patient's fragile skin. Non-contact video-based patient monitoring systems are therefore a preferrable solution. While a multitude of high-performing machine vision technologies have been successfully implemented on an adult population, such methods often fail in neonatal population. In this thesis, we assess state-of-the-art adult-based methods to bridge the gap to an understudied neonatal population in the NICU environment. To this end, several important machine vision concepts are investigated, including scene understanding, image classification, face detection, semantic segmentation, motion detection, face tracking, and heart rate estimation. In each of these areas, we assess the state-of-the-art and identify its applicability to a neonatal population. In cases where serious limitations are observed, this thesis pushes the state-of-the-art

Uncertainty Measurements in Non-Contact Neonatal Heart Rate Monitoring

“…Based on actual patient data from the NICU, we herein demonstrate how these challenges arise during continuous monitoring and how such scenarios can impact each stage in the monitoring pipeline. Portions of this paper are based on this thesis [5].…”