Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70787
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dc.contributorDepartment of Computing-
dc.creatorKou, L-
dc.creatorZhang, D-
dc.creatorLiu, DX-
dc.date.accessioned2017-12-28T06:18:08Z-
dc.date.available2017-12-28T06:18:08Z-
dc.identifier.issn1424-8220-
dc.identifier.urihttp://hdl.handle.net/10397/70787-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2017 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Kou, L., Zhang, D., & Liu, D. X. (2017). A novel medical E-nose signal analysis system. Sensors, 17(4), (Suppl. ), 402, - is available athttps://dx.doi.org/10.3390/s17040402en_US
dc.subjectE-noseen_US
dc.subjectChemical sensorsen_US
dc.subjectBreath analysisen_US
dc.subjectBlood glucose levelen_US
dc.titleA novel medical E-nose signal analysis systemen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume17-
dc.identifier.issue4-
dc.identifier.doi10.3390/s17040402-
dcterms.abstractIt has been proven that certain biomarkers in people's breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, Apr. 2017, v. 17, no. 4, 402, p. 1-15-
dcterms.isPartOfSensors-
dcterms.issued2017-
dc.identifier.isiWOS:000400822900001-
dc.identifier.ros2016002510-
dc.identifier.artn402-
dc.identifier.rosgroupid2016002458-
dc.description.ros2016-2017 > Academic research: refereed > Publication in refereed journal-
dc.description.validatebcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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