Respiration monitoring for premature neonates in NICU

Yue Sun, Wenjin Wang, Xi Long, Mohammed Meftah, Tao Tan, Caifeng Shan, Ronald M. Aarts, Peter H.N. de With

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Article number5246
JournalApplied Sciences (Switzerland)
Issue number23
Publication statusPublished - 1 Dec 2019
Externally publishedYes


  • Biomedical monitoring
  • Remote sensing
  • Respiration
  • Respiration rate
  • Video processing


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