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. 2024 Oct 30:12:684-696.
doi: 10.1109/JTEHM.2024.3488523. eCollection 2024.

Video-Based Respiratory Rate Estimation for Infants in the NICU

Soodeh Ahani  1   2 , Nikoo Niknafs  2   3 , Pascal M Lavoie  2   3 , Liisa Holsti  2   4 , Guy A Dumont  1   2   5
Affiliations

Video-Based Respiratory Rate Estimation for Infants in the NICU

Soodeh Ahani et al. IEEE J Transl Eng Health Med. .

Abstract

Objective: Non-contact respiratory rate estimation (RR) is highly desirable for infants because of their sensitive skin. We propose a novel RGB video-based RR estimation method for infants in the neonatal intensive care unit (NICU) that can accurately measure the RR contact-less.

Methods and procedures: We utilize Eulerian video magnification (EVM) method and develop an adaptive peak prominence threshold value estimation method to address challenges of RR estimation (e.g., dark environments, shallow breathing, babies swaddled or under blankets). We recruited 13 infants recorded for 4 consecutive hours per case. We then evaluate the performance of the algorithm for several (i.e., 19 to 25) randomly selected videos, each lasting 1 minute, for each case.

Results: Intraclass correlation coefficients of the proposed method over manually and automatically selected ROIs are 0.91 (95%CI: [Formula: see text]) and 0.88 (95%CI: [Formula: see text]), indicating excellent and good reliability, respectively. The Bland-Altman analysis of the proposed algorithm shows higher agreement between the estimated values via the proposed method and visually counted RR than the agreement between the RR obtained from the impedance sensors and reference RR, and agreement between a former EVM-based method and reference RR values.

Conclusion: Our algorithm shows promising results for RR estimation in a real-life NICU environment under various conditions that can confound the estimation.

Clinical impact: We present a robust algorithm for non-contact neonatal respiratory rate monitoring, capable of performing well under various environmental lighting conditions in NICU, even when the infant is clothed or covered.

Keywords: Eulerian video magnification; Respiratory rate estimation; infants; motion magnification; video-based monitoring.

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Figures

FIGURE 1.
FIGURE 1.
NICU setup for video image acquisition and data collection.
FIGURE 2.
FIGURE 2.
Representative frame samples captured in two different lighting conditions; (a) in a lit environment, and (b) in a low-lit environment.
FIGURE 3.
FIGURE 3.
The block diagram of the proposed EVM-based RR estimation method.
FIGURE 4.
FIGURE 4.
The block diagram of the proposed automatic ROI selection method.
FIGURE 5.
FIGURE 5.
The sliding block over the frames and the generated signal formula image for the formula imageth image-block formula image.
FIGURE 6.
FIGURE 6.
An example of the ROIs selected using the proposed automatic ROI selection algorithm.
FIGURE 7.
FIGURE 7.
An example of the upper and lower envelopes of the breath pulse for automatic peak prominence threshold value estimation.
FIGURE 8.
FIGURE 8.
BA plots of the RR estimated by using the proposed method over the manually formula image, and automatically formula image selected ROIs compared with the BA plot of the manual reference values formula image and formula image.
FIGURE 9.
FIGURE 9.
A sample breath pulse and detected breath pause which last 2.09 s when the threshold is set to formula image.
See this image and copyright information in PMC

References

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    1. Brieva J., Ponce H., and Moya-Albor E., “A contactless respiratory rate estimation method using a Hermite magnification technique and convolutional neural networks,” Appl. Sci., vol. 10, no. 2, p. 607, 2020.

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