Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring

We introduce a dataset to provide insights into the relationship between the diaphragm surface electromyographic (sEMG) signal and the respiratory air flow. The data presented had been originally collected for a research project jointly developed by the Department of Information Engineering and the...

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Autores principales: Biagetti, Giorgio, Carnielli, Virgilio Paolo, Crippa, Paolo, Falaschetti, Laura, Scacchia, Valentina, Scalise, Lorenzo, Turchetti, Claudio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Engineering
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698777/
https://www.ncbi.nlm.nih.gov/pubmed/31440545
http://dx.doi.org/10.1016/j.dib.2019.104217
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author Biagetti, Giorgio
Carnielli, Virgilio Paolo
Crippa, Paolo
Falaschetti, Laura
Scacchia, Valentina
Scalise, Lorenzo
Turchetti, Claudio
author_facet Biagetti, Giorgio
Carnielli, Virgilio Paolo
Crippa, Paolo
Falaschetti, Laura
Scacchia, Valentina
Scalise, Lorenzo
Turchetti, Claudio
author_sort Biagetti, Giorgio
collection PubMed
description We introduce a dataset to provide insights into the relationship between the diaphragm surface electromyographic (sEMG) signal and the respiratory air flow. The data presented had been originally collected for a research project jointly developed by the Department of Information Engineering and the Department of Industrial Enginering and Mathematical Sciences, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 8 subjects, and includes 8 air flow and 8 surface electromyographic (sEMG) signals for diaphragmatic respiratory activity monitoring, measured with a sampling frequency of 2 kHz.
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spelling pubmed-66987772019-08-22 Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring Biagetti, Giorgio Carnielli, Virgilio Paolo Crippa, Paolo Falaschetti, Laura Scacchia, Valentina Scalise, Lorenzo Turchetti, Claudio Data Brief Engineering We introduce a dataset to provide insights into the relationship between the diaphragm surface electromyographic (sEMG) signal and the respiratory air flow. The data presented had been originally collected for a research project jointly developed by the Department of Information Engineering and the Department of Industrial Enginering and Mathematical Sciences, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 8 subjects, and includes 8 air flow and 8 surface electromyographic (sEMG) signals for diaphragmatic respiratory activity monitoring, measured with a sampling frequency of 2 kHz. Elsevier 2019-07-05 /pmc/articles/PMC6698777/ /pubmed/31440545 http://dx.doi.org/10.1016/j.dib.2019.104217 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Engineering
Biagetti, Giorgio
Carnielli, Virgilio Paolo
Crippa, Paolo
Falaschetti, Laura
Scacchia, Valentina
Scalise, Lorenzo
Turchetti, Claudio
Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
title Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
title_full Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
title_fullStr Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
title_full_unstemmed Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
title_short Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
title_sort dataset from spirometer and semg wireless sensor for diaphragmatic respiratory activity monitoring
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698777/
https://www.ncbi.nlm.nih.gov/pubmed/31440545
http://dx.doi.org/10.1016/j.dib.2019.104217
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