BackgroundMonitoring of vital parameters is an important topic in neonatal daily care. Progress in computational intelligence and medical sensors has facilitated the development of smart bedside monitors that can integrate multiple parameters into a single monitoring system. This paper describes non-contact monitoring of neonatal vital signals based on infrared thermography as a new biomedical engineering application. One signal of clinical interest is the spontaneous respiration rate of the neonate. It will be shown that the respiration rate of neonates can be monitored based on analysis of the anterior naris (nostrils) temperature profile associated with the inspiration and expiration phases successively.ObjectiveThe aim of this study is to develop and investigate a new non-contact respiration monitoring modality for neonatal intensive care unit (NICU) using infrared thermography imaging. This development includes subsequent image processing (region of interest (ROI) detection) and optimization. Moreover, it includes further optimization of this non-contact respiration monitoring to be considered as physiological measurement inside NICU wards.ResultsContinuous wavelet transformation based on Debauches wavelet function was applied to detect the breathing signal within an image stream. Respiration was successfully monitored based on a 0.3°C to 0.5°C temperature difference between the inspiration and expiration phases.ConclusionsAlthough this method has been applied to adults before, this is the first time it was used in a newborn infant population inside the neonatal intensive care unit (NICU). The promising results suggest to include this technology into advanced NICU monitors.

Monitoring of respiratory rate (RR) is very important for patient assessment. In fact, it is considered one of the relevant vital parameters in critical care medicine. Nowadays, standard monitoring relies on obtrusive and invasive techniques which require adhesive electrodes or sensors to be attached to the patient's body. Unfortunately, these procedures cause stress, pain and frequently damage the vulnerable skin of preterm infants. The current paper presents a "black-box" algorithm for remote monitoring of RR in thermal videos. "Black-box" in this context means that the algorithm does not rely on tracking of specific anatomic landmarks. Instead, it automatically distinguishes regions of interest in the video containing the respiratory signal from those containing only noise. To examine its performance and robustness during physiological (phase A) and pathological scenarios (phase B), a study on twelve healthy volunteers was carried out. After a successful validation on adults, a clinical study on eight newborn infants was conducted. A good agreement between estimated RR and ground truth was achieved. In the study involving adult volunteers, a mean root-mean-square error (RMSE) of 0.31 ± 0.09 breaths/min and 3.27 ± 0.72 breaths/min was obtained for phase A and phase B, respectively. In the study involving infants, the mean RMSE hovered around 4.15 ± 1.44 breaths/min. In brief, this paper demonstrates that infrared thermography might become a clinically relevant alternative for the currently available RR monitoring modalities in neonatal care.