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Type 2 diabetes screening test by means of a pulse oximeter
dc.contributor.author | Monte Moreno, Enrique |
dc.contributor.author | Anyo Luján, María José |
dc.contributor.author | Torres Rusiñol, Montse |
dc.contributor.author | Juarez Fernández, Paqui |
dc.contributor.author | Núñez Manrique, Pilar |
dc.contributor.author | Aragón Triviño, Cristina |
dc.contributor.author | Pedrosa Miquel, Magda |
dc.contributor.author | Álvarez-Rodríguez, Marife |
dc.contributor.author | González-Burguillos, María José |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2017-01-13T10:08:24Z |
dc.date.available | 2017-01-13T10:08:24Z |
dc.date.issued | 2016-04-20 |
dc.identifier.citation | Monte, E., Anyo, M., Torres, M., Juarez, P., Núñez, P., Aragón, C., Pedrosa, M., Álvarez-Rodríguez, M., González-Burguillos, M. Type 2 diabetes screening test by means of a pulse oximeter. "IEEE transactions on biomedical engineering", Vol. 64 (2). Feb 2017. |
dc.identifier.issn | 0018-9294 |
dc.identifier.uri | http://hdl.handle.net/2117/99202 |
dc.description.abstract | In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts; the first analyses the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i.e. whether the subject had diabetes or not. The machine-learning algorithms were random forests, gradient boosting, and linear discriminant analysis as benchmark. The system was tested on a database of 1, 157 subjects (two samples per subject) collected from five community health centres. The mean receiver operating characteristic (ROC) area found was 69.4% (median value 71.9% and range [75.4%-61.1%]), with a specificity=64% for a threshold that gave a sensitivity=65%. We present a screening method for detecting diabetes that has a performance comparable to the glycated haemoglobin (haemoglobin A1c HbA1c) test, does not require blood extraction, and yields results in less than five minutes. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics |
dc.subject.lcsh | Biomedical engineering |
dc.subject.other | Type 2 Diabetes |
dc.subject.other | Screening |
dc.subject.other | Machine learning |
dc.subject.other | Statistical learning |
dc.subject.other | Boosting |
dc.subject.other | Photoplethysmography |
dc.subject.other | Signal processing |
dc.subject.other | Noninvasive treatment |
dc.title | Type 2 diabetes screening test by means of a pulse oximeter |
dc.type | Article |
dc.subject.lemac | Enginyeria biomèdica |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.1109/TBME.2016.2554661 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/aboutJournal.jsp?punumber=10 |
dc.rights.access | Open Access |
local.identifier.drac | 19354515 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Monte, E.; Anyo, M.; Torres, M.; Juarez, P.; Núñez, P.; Aragón, C.; Pedrosa, M.; Álvarez-Rodríguez, M.; González-Burguillos, M. |
local.citation.publicationName | IEEE transactions on biomedical engineering |
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