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Convolutional neural network-based respiration analysis of electrical activities of the diaphragm

The electrical activity of the diaphragm (Edi) is considered a new respiratory vital sign for monitoring breathing patterns and efforts during ventilator care. However, the Edi signal contains irregular noise from complex causes, which makes reliable breathing analysis difficult. Deep learning was i...

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Autores principales: Lee, Hyun-Gyu, Lee, Gahee, Lee, Juyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534871/
https://www.ncbi.nlm.nih.gov/pubmed/36198756
http://dx.doi.org/10.1038/s41598-022-21165-9
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author Lee, Hyun-Gyu
Lee, Gahee
Lee, Juyoung
author_facet Lee, Hyun-Gyu
Lee, Gahee
Lee, Juyoung
author_sort Lee, Hyun-Gyu
collection PubMed
description The electrical activity of the diaphragm (Edi) is considered a new respiratory vital sign for monitoring breathing patterns and efforts during ventilator care. However, the Edi signal contains irregular noise from complex causes, which makes reliable breathing analysis difficult. Deep learning was implemented to accurately detect the Edi signal peaks and analyze actual neural breathing in premature infants. Edi signals were collected from 17 premature infants born before gestational age less than 32 weeks, who received ventilatory support with a non-invasive neurally adjusted ventilatory assist. First, a local maximal detection method that over-detects candidate Edi peaks was used. Subsequently, a convolutional neural network-based deep learning was implemented to classify candidates into final Edi peaks. Our approach showed superior performance in all aspects of respiratory Edi peak detection and neural breathing analysis compared with the currently used recording technique in the ventilator. The method obtained a f1-score of 0.956 for the Edi peak detection performance and [Formula: see text] value of 0.823 for respiratory rates based on the number of Edi peaks. The proposed technique can achieve a more reliable analysis of Edi signals, including evaluation of the respiration rate in premature infants.
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spelling pubmed-95348712022-10-07 Convolutional neural network-based respiration analysis of electrical activities of the diaphragm Lee, Hyun-Gyu Lee, Gahee Lee, Juyoung Sci Rep Article The electrical activity of the diaphragm (Edi) is considered a new respiratory vital sign for monitoring breathing patterns and efforts during ventilator care. However, the Edi signal contains irregular noise from complex causes, which makes reliable breathing analysis difficult. Deep learning was implemented to accurately detect the Edi signal peaks and analyze actual neural breathing in premature infants. Edi signals were collected from 17 premature infants born before gestational age less than 32 weeks, who received ventilatory support with a non-invasive neurally adjusted ventilatory assist. First, a local maximal detection method that over-detects candidate Edi peaks was used. Subsequently, a convolutional neural network-based deep learning was implemented to classify candidates into final Edi peaks. Our approach showed superior performance in all aspects of respiratory Edi peak detection and neural breathing analysis compared with the currently used recording technique in the ventilator. The method obtained a f1-score of 0.956 for the Edi peak detection performance and [Formula: see text] value of 0.823 for respiratory rates based on the number of Edi peaks. The proposed technique can achieve a more reliable analysis of Edi signals, including evaluation of the respiration rate in premature infants. Nature Publishing Group UK 2022-10-05 /pmc/articles/PMC9534871/ /pubmed/36198756 http://dx.doi.org/10.1038/s41598-022-21165-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lee, Hyun-Gyu
Lee, Gahee
Lee, Juyoung
Convolutional neural network-based respiration analysis of electrical activities of the diaphragm
title Convolutional neural network-based respiration analysis of electrical activities of the diaphragm
title_full Convolutional neural network-based respiration analysis of electrical activities of the diaphragm
title_fullStr Convolutional neural network-based respiration analysis of electrical activities of the diaphragm
title_full_unstemmed Convolutional neural network-based respiration analysis of electrical activities of the diaphragm
title_short Convolutional neural network-based respiration analysis of electrical activities of the diaphragm
title_sort convolutional neural network-based respiration analysis of electrical activities of the diaphragm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534871/
https://www.ncbi.nlm.nih.gov/pubmed/36198756
http://dx.doi.org/10.1038/s41598-022-21165-9
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