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Authors: Ninah Koolen 1 ; Olivier Decroupet 2 ; Anneleen Dereymaeker 2 ; Katrien Jansen 2 ; Jan Vervisch 2 ; Vladimir Matic 1 ; Bart Vanrumste 3 ; Gunnar Naulaers 2 ; Sabine Van Huffel 1 and Maarten De Vos 4

Affiliations: 1 University of Leuven and iMinds-KU Leuven, Belgium ; 2 University of Leuven, Belgium ; 3 University of Leuven, iMinds-KU Leuven and University of Leuven campus Geel, Belgium ; 4 University of Oldenburg and University of Oxford, Germany

ISBN: 978-989-758-077-2

Keyword(s): Automated Respiration Detection, Neonatal Care, Polysomnography, Video, Optical Flow Algorithm, Eulerian Video Magnification.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Human-Computer Interaction ; Image and Video Analysis ; Medical Imaging ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Physiological Computing Systems ; Software Engineering ; Video Analysis

Abstract: In the interest of the neonatal comfort, the need for noncontact respiration monitoring increases. Moreover, home respiration monitoring would be beneficial. Therefore, the goal is to extract the respiration rate from video data included in a polysomnography. The presented method first uses Eulerian video magnification to amplify the respiration movements. A respiration signal is obtained through the optical flow algorithm. Independent component analysis and principal component analysis are applied to improve the signal quality, with minor enhancement of the signal quality. The respiratory rate is extracted as the dominant frequency in the spectrograms obtained using the short-time Fourier transform. Respiratory rate detection is successful (94.12%) for most patients during quiet sleep stages. Real-time monitoring could possibly be achieved by lowering the spatial and temporal resolutions of the input video data. The outline for successful video-aided detection of the respiration patt ern is shown, thereby paving the way for improvement of the overall assessment in the NICU and application in a home-friendly environment. (More)

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Paper citation in several formats:
Koolen, N.; Decroupet, O.; Dereymaeker, A.; Jansen, K.; Vervisch, J.; Matic, V.; Vanrumste, B.; Naulaers, G.; Van Huffel, S. and De Vos, M. (2015). Automated Respiration Detection from Neonatal Video Data.In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 164-169. DOI: 10.5220/0005187901640169

@conference{icpram15,
author={Ninah Koolen. and Olivier Decroupet. and Anneleen Dereymaeker. and Katrien Jansen. and Jan Vervisch. and Vladimir Matic. and Bart Vanrumste. and Gunnar Naulaers. and Sabine Van Huffel. and Maarten De Vos.},
title={Automated Respiration Detection from Neonatal Video Data},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={164-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005187901640169},
isbn={978-989-758-077-2},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Automated Respiration Detection from Neonatal Video Data
SN - 978-989-758-077-2
AU - Koolen, N.
AU - Decroupet, O.
AU - Dereymaeker, A.
AU - Jansen, K.
AU - Vervisch, J.
AU - Matic, V.
AU - Vanrumste, B.
AU - Naulaers, G.
AU - Van Huffel, S.
AU - De Vos, M.
PY - 2015
SP - 164
EP - 169
DO - 10.5220/0005187901640169

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