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Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection

Both Respiratory Flow (RF) and Respiratory Motion (RM) are visible in thermal recordings of infants. Monitoring these two signals usually requires landmark detection for the selection of a region of interest. Other approaches combine respiratory signals coming from both RF and RM, obtaining a Mixed...

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Autores principales: Lorato, Ilde, Stuijk, Sander, Meftah, Mohammed, Kommers, Deedee, Andriessen, Peter, van Pul, Carola, de Haan, Gerard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472592/
https://www.ncbi.nlm.nih.gov/pubmed/34577513
http://dx.doi.org/10.3390/s21186306
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author Lorato, Ilde
Stuijk, Sander
Meftah, Mohammed
Kommers, Deedee
Andriessen, Peter
van Pul, Carola
de Haan, Gerard
author_facet Lorato, Ilde
Stuijk, Sander
Meftah, Mohammed
Kommers, Deedee
Andriessen, Peter
van Pul, Carola
de Haan, Gerard
author_sort Lorato, Ilde
collection PubMed
description Both Respiratory Flow (RF) and Respiratory Motion (RM) are visible in thermal recordings of infants. Monitoring these two signals usually requires landmark detection for the selection of a region of interest. Other approaches combine respiratory signals coming from both RF and RM, obtaining a Mixed Respiratory (MR) signal. The detection and classification of apneas, particularly common in preterm infants with low birth weight, would benefit from monitoring both RF and RM, or MR, signals. Therefore, we propose in this work an automatic RF pixel detector not based on facial/body landmarks. The method is based on the property of RF pixels in thermal videos, which are in areas with a smooth circular gradient. We defined 5 features combined with the use of a bank of Gabor filters that together allow selection of the RF pixels. The algorithm was tested on thermal recordings of 9 infants amounting to a total of 132 min acquired in a neonatal ward. On average the percentage of correctly identified RF pixels was [Formula: see text]. Obstructive Apneas (OAs) were simulated as a proof of concept to prove the advantage in monitoring the RF signal compared to the MR signal. The sensitivity in the simulated OA detection improved for the RF signal reaching [Formula: see text] against the [Formula: see text] of the MR signal. Overall, the method yielded promising results, although the positioning and number of cameras used could be further optimized for optimal RF visibility.
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spelling pubmed-84725922021-09-28 Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection Lorato, Ilde Stuijk, Sander Meftah, Mohammed Kommers, Deedee Andriessen, Peter van Pul, Carola de Haan, Gerard Sensors (Basel) Article Both Respiratory Flow (RF) and Respiratory Motion (RM) are visible in thermal recordings of infants. Monitoring these two signals usually requires landmark detection for the selection of a region of interest. Other approaches combine respiratory signals coming from both RF and RM, obtaining a Mixed Respiratory (MR) signal. The detection and classification of apneas, particularly common in preterm infants with low birth weight, would benefit from monitoring both RF and RM, or MR, signals. Therefore, we propose in this work an automatic RF pixel detector not based on facial/body landmarks. The method is based on the property of RF pixels in thermal videos, which are in areas with a smooth circular gradient. We defined 5 features combined with the use of a bank of Gabor filters that together allow selection of the RF pixels. The algorithm was tested on thermal recordings of 9 infants amounting to a total of 132 min acquired in a neonatal ward. On average the percentage of correctly identified RF pixels was [Formula: see text]. Obstructive Apneas (OAs) were simulated as a proof of concept to prove the advantage in monitoring the RF signal compared to the MR signal. The sensitivity in the simulated OA detection improved for the RF signal reaching [Formula: see text] against the [Formula: see text] of the MR signal. Overall, the method yielded promising results, although the positioning and number of cameras used could be further optimized for optimal RF visibility. MDPI 2021-09-21 /pmc/articles/PMC8472592/ /pubmed/34577513 http://dx.doi.org/10.3390/s21186306 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lorato, Ilde
Stuijk, Sander
Meftah, Mohammed
Kommers, Deedee
Andriessen, Peter
van Pul, Carola
de Haan, Gerard
Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection
title Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection
title_full Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection
title_fullStr Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection
title_full_unstemmed Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection
title_short Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection
title_sort automatic separation of respiratory flow from motion in thermal videos for infant apnea detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472592/
https://www.ncbi.nlm.nih.gov/pubmed/34577513
http://dx.doi.org/10.3390/s21186306
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