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Open AccessArticle

Respiration Monitoring for Premature Neonates in NICU

1
Department of Electrical Engineering, Eindhoven University of Technology, 5612 WH Eindhoven, The Netherlands
2
Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5246; https://doi.org/10.3390/app9235246
Received: 18 October 2019 / Revised: 25 November 2019 / Accepted: 28 November 2019 / Published: 2 December 2019
(This article belongs to the Section Applied Physics)
In this paper, we investigate an automated pipeline to estimate respiration signals from videos for premature infants in neonatal intensive care units (NICUs). Two flow estimation methods, namely the conventional optical flow- and deep learning-based flow estimation methods, were employed and compared to estimate pixel motion vectors between adjacent video frames. The respiratory signal is further extracted via motion factorization. The proposed methods were evaluated by comparing our automated extracted respiration signals to that extracted from chest impedance on videos of five premature infants. The overall average cross-correlation coefficients are 0.70 for the optical flow-based method and 0.74 for the deep flow-based method. The average root mean-squared errors are 6.10 and 4.55 for the optical flow- and the deep flow-based methods, respectively. The experimental results are promising for further investigation and clinical application of the video-based respiration monitoring method for infants in NICUs. View Full-Text
Keywords: respiration; respiration rate; video processing; remote sensing; biomedical monitoring respiration; respiration rate; video processing; remote sensing; biomedical monitoring
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Sun, Y.; Wang, W.; Long, X.; Meftah, M.; Tan, T.; Shan, C.; Aarts, R.M.; de With, P.H.N. Respiration Monitoring for Premature Neonates in NICU. Appl. Sci. 2019, 9, 5246.

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