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

• Published in: Data in brief Vol. 35; p. 106767
• Main Authors: Durán Acevedo, Cristhian Manuel, Cuastumal Vasquez, Carlos A., Carrillo Gómez, Jeniffer Katerine
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.04.2021; Elsevier
• Subjects: COPD; Data; Electronic nose; Exhaled breath; Gas sensor; Machine learning; Gas sensor; Electronic nose; Machine learning; Exhaled breath; COPD
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2021.106767

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)