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. 2020 Oct 6;7(10):171.
doi: 10.3390/children7100171.

Non-Contact Video-Based Neonatal Respiratory Monitoring

Scott L Rossol  1 , Jeffrey K Yang  1 , Caroline Toney-Noland  1 , Janine Bergin  1 , Chandan Basavaraju  2 , Pavan Kumar  2 , Henry C Lee  1
Affiliations

Non-Contact Video-Based Neonatal Respiratory Monitoring

Scott L Rossol et al. Children (Basel). .

Abstract

Respiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system that could improve patient care. This iterative design study developed a novel algorithm that produced RR from footage analyzed from stable NICU patients in open cribs with corrected gestational ages ranging from 33 to 40 weeks. The final algorithm used a proprietary technique of micromotion and stationarity detection (MSD) to model background noise to be able to amplify and record respiratory motions. We found significant correlation-r equals 0.948 (p value of 0.001)-between MSD and the current hospital standard, electrocardiogram impedance pneumography. Our video-based system showed a bias of negative 1.3 breaths and root mean square error of 6.36 breaths per minute compared to standard continuous monitoring. Further work is needed to evaluate the ability of video-based monitors to observe clinical changes in a larger population of patients over extended periods of time.

Keywords: biomedical technology; clinical alarms; neonatal monitoring; respiratory rate; video recording.

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Conflict of interest statement

Authors Chandan Basavaraju and Pavan Kumar were members of a startup company designing a consumer baby monitor with the goal of utilizing the final design of this study. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Study setup. Red circle shows camera set up above an open neonatal intensive care units (NICU) crib. The hospital-standard monitor can be seen on the opposite side of the crib as the camera.
Figure 2
Figure 2
Heat map derived from standard deviation (SD) measures of SD measures showing motion due to breathing. The red region represents high SD measures and the blue region represents low SD measures. The red region was concentrated near the baby’s chest, indicating that the measurement showed motion associated with breathing.
Figure 3
Figure 3
(a) Respiratory rate (RR) (y-axis) from the video monitoring system compared to that of the extracted Electronic medical record (EMR) data over a 8.4 min recording made up of 90 time points (x-axis); (b) RR (y-axis) from the video monitoring system compared to that of the extracted EMR data over a 13.4 min recording made up of 155 time points (x-axis)
Figure 4
Figure 4
Bland–Altman plot. The central dark line represents a bias of −1.3 breaths per minute (BPM). The dashed lines represent the upper limit of agreement (10.9 BPM) and lower limit of agreement (−13.5 BPM).
Figure 5
Figure 5
Linear regression comparing video-based monitoring respiration rate (RR) (y-axis) vs. electronic medical record (EMR)- RR (x-axis). The dashed lines represent upper and lower boundaries of root mean square error between the modes of measurement.
See this image and copyright information in PMC

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