Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis

This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmon...

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Autores principales: Durán Acevedo, Cristhian Manuel, Cuastumal Vasquez, Carlos A., Carrillo Gómez, Jeniffer Katerine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Data Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838708/
https://www.ncbi.nlm.nih.gov/pubmed/33537382
http://dx.doi.org/10.1016/j.dib.2021.106767
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author Durán Acevedo, Cristhian Manuel
Cuastumal Vasquez, Carlos A.
Carrillo Gómez, Jeniffer Katerine
author_facet Durán Acevedo, Cristhian Manuel
Cuastumal Vasquez, Carlos A.
Carrillo Gómez, Jeniffer Katerine
author_sort Durán Acevedo, Cristhian Manuel
collection PubMed
description This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis. The database consists of 4 groups of measurements which were acquired through the E-nose system: 10 control samples (healthy people), 20 samples of people with COPD, 4 samples of smokers and 10 air samples, where in each group two samples of exhaled breath per person were acquired giving a total of 78 samples (40 from COPD, 20 from control, 8 from smokers and 10 from the air)
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spelling pubmed-78387082021-02-02 Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis Durán Acevedo, Cristhian Manuel Cuastumal Vasquez, Carlos A. Carrillo Gómez, Jeniffer Katerine Data Brief Data Article This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis. The database consists of 4 groups of measurements which were acquired through the E-nose system: 10 control samples (healthy people), 20 samples of people with COPD, 4 samples of smokers and 10 air samples, where in each group two samples of exhaled breath per person were acquired giving a total of 78 samples (40 from COPD, 20 from control, 8 from smokers and 10 from the air) Elsevier 2021-01-18 /pmc/articles/PMC7838708/ /pubmed/33537382 http://dx.doi.org/10.1016/j.dib.2021.106767 Text en © 2021 The Authors 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 Data Article
Durán Acevedo, Cristhian Manuel
Cuastumal Vasquez, Carlos A.
Carrillo Gómez, Jeniffer Katerine
Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis
title Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis
title_full Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis
title_fullStr Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis
title_full_unstemmed Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis
title_short Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis
title_sort electronic nose dataset for copd detection from smokers and healthy people through exhaled breath analysis
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838708/
https://www.ncbi.nlm.nih.gov/pubmed/33537382
http://dx.doi.org/10.1016/j.dib.2021.106767
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