Non-contact monitoring of respiration in the neonatal intensive care unit

An abnormal respiratory rhythm is an early indicator of physiological deterioration. It is of critical importance in the clinical management of critically-ill or premature infants, for whom apnoea of prematurity is a major concern. Nevertheless, respiratory signals are still largely disregarded in neonatal intensive care units due to the high prevalence of noise and high false alarm rates in conventional monitoring. To address this, we present a novel method for the extraction of respiration from camera-based measurements taken from the top-view of an incubator. A total of 107 events from 30 neonatal admissions were annotated by three clinical reviewers as either true cessations of breathing (physiologically relevant) or false (artefact-related). The events were divided into two independent groups for training and validation and our algorithm was trained to classify true cessations. We achieved a good classification performance with 9 out of 10 cessations and 7 out of 10 artefactual events correctly identified in the training set, and with 7 out of 10 cessations and 34 out of 44 artefactual events correctly identified in the out-of-sample test set. A reduction in false alarm rate of 77.3% was achieved.