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Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm

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Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm

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dc.contributor.author Cuesta Frau, David es_ES
dc.contributor.author Miró Martínez, Pau es_ES
dc.contributor.author Oltra Crespo, Sandra es_ES
dc.contributor.author Jordán Núñez, Jorge es_ES
dc.contributor.author Vargas-Rojo, B. es_ES
dc.contributor.author Vigil-Medina, Luis es_ES
dc.date.accessioned 2019-06-12T20:42:08Z
dc.date.available 2019-06-12T20:42:08Z
dc.date.issued 2018 es_ES
dc.identifier.issn 0169-2607 es_ES
dc.identifier.uri http://hdl.handle.net/10251/122037
dc.description.abstract [EN] Background and objectives : The adoption in clinical practice of electronic portable blood or interstitial glucose monitors has enabled the collection, storage, and sharing of massive amounts of glucose level readings. This availability of data opened the door to the application of a multitude of mathematical methods to extract clinical information not discernible with conventional visual inspection. The objective of this study is to assess the capability of Permutation Entropy (PE) to find differences between glucose records of healthy and potentially diabetic subjects. Methods : PE is a mathematical method based on the relative frequency analysis of ordinal patterns in time series that has gained a lot of attention in the last years due to its simplicity, robustness, and per- formance. We study in this paper the applicability of this method to glucose records of subjects at risk of diabetes in order to assess the predictability value of this metric in this context. Results : PE, along with some of its derivatives, was able to find significant differences between diabetic and non¿diabetic patients from records acquired up to 3 years before the diagnosis. The quantitative results for PE were 3.5878 ±0.3916 for the nondiabetic class, and 3.1564 ±0.4166 for the diabetic class. With a classification accuracy higher than 70%, and by means of a Cox regression model, PE demonstrated that it is a very promising candidate as a risk stratification tool for continuous glucose monitoring. Conclusion : PE can be considered as a prospective tool for the early diagnosis of the glucoregulatory system. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Permutation entropy es_ES
dc.subject Continuous glucose monitoring es_ES
dc.subject Signal classification es_ES
dc.subject Diabetes es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2018.08.018 es_ES
dc.rights.accessRights Abierto es_ES
dc.date.embargoEndDate 2019-10-01 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Cuesta Frau, D.; Miró Martínez, P.; Oltra Crespo, S.; Jordán Núñez, J.; Vargas-Rojo, B.; Vigil-Medina, L. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine. 165:197-204. https://doi.org/10.1016/j.cmpb.2018.08.018 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1016/j.cmpb.2018.08.018 es_ES
dc.description.upvformatpinicio 197 es_ES
dc.description.upvformatpfin 204 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 165 es_ES
dc.identifier.pmid 30337074
dc.relation.pasarela S\371846 es_ES


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