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http://hdl.handle.net/10662/21443
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Campo DC | Valor | idioma |
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dc.contributor.author | Alvarado Valiente, Jaime | - |
dc.contributor.author | Velasco, J. Manuel | - |
dc.contributor.author | Chávez de la O, Francisco | - |
dc.contributor.author | Fernández de Vega, Francisco, 1971- | - |
dc.contributor.author | Hidalgo Pérez, José Ignacio | - |
dc.date.accessioned | 2024-06-06T16:48:52Z | - |
dc.date.available | 2024-06-06T16:48:52Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 0169-7439 | - |
dc.identifier.uri | http://hdl.handle.net/10662/21443 | - |
dc.description.abstract | Estimating future blood glucose levels is an essential and challenging task for people with diabetes. It must be carried out based on variables such as current glucose, carbohydrate intake, physical activity, and insulin dosing. Accurate estimation is essential to maintain glucose values in a healthy range and avoid dangerous events of low glucose levels (hypoglycemia) and extremely high glucose values (hyperglycemia). Those situations maintained in time can cause not only permanent long-term damage but also short-term complications and even the death of the person. This paper proposes a new method to predict and detect hypoglycemic events over a 24-h time horizon. The technique combines applying the wavelet transform to glucose time series and deep learning convolutional neural networks. We have experimented with real data collected from 20 different people with type 1 diabetes. Our technique can also be applied to predict hyperglycemia. We incorporate a data augmentation technique consisting of a rolling windows system that improves the accuracy of the prediction. The uncertainty of the data is considered by the addition of controlled noise. The results show that the predictions obtained are accurate (higher than 88% of accuracy, sensitivity, specificity, and precision), confirming the effectiveness of the proposed method. | es_ES |
dc.description.sponsorship | This work has been supported by Spanish Ministerio de Ciencia e Innovación - grants PID2021-125549OB-I00, PDC2022-133429-I00, and PID2020-115570GB-C21, Junta de Extremadura, Consejería de Economía e Infraestructuras, del Fondo Europeo de Desarrollo Regional, “Una manera de hacer Europa”, grant GR21108. Devices for adquiring data from patients were adquired under the support of Fundación Eugenio Rodriguez Pascual 2019 grant - Desarrollo de sistemas adaptativos y bioinspirados para el control glucémico con infusores subcutáneos continuos de insulina y monitores continuos de glucosa (Development of adaptive and bioinspired systems for glycaemic control with continuous subcutaneous insulin infusors and continuous glucose monitors). We acknowledge computing support from Madrid Regional Government and FEDER funds under grant Y2018/NMT-4668 (Micro-Stress- MAP-CM). | es_ES |
dc.format.extent | 14 p. | es_ES |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Diabetes | es_ES |
dc.subject | Predicción de glucosa | es_ES |
dc.subject | Aprendizaje profundo | es_ES |
dc.subject | Transformada Wavelet | es_ES |
dc.subject | Glucose prediction | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Wavelet transform | es_ES |
dc.title | Combining wavelet transform with convolutional neural networks for hypoglycemia events prediction from CGM data | es_ES |
dc.type | article | es_ES |
dc.description.version | peerReviewed | es_ES |
europeana.type | TEXT | en_US |
dc.rights.accessRights | openAccess | es_ES |
dc.subject.unesco | 3205.04 Hematología | es_ES |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | Alvarado, J., Velasco, J.M., Chávez, F., Fernández de Vega, F. (2023). Combining wavelet transform with convolutional neural networks for hypoglycemia events prediction from CGM data. Chemometrics and Intelligent Laboratory Systems, 243. 105017. https://doi.org/10.1016/j.chemolab.2023.105017 | es_ES |
dc.type.version | publishedVersion | es_ES |
dc.contributor.affiliation | N/A | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Ingeniería de Sistemas Informáticos y Telemáticos | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0169743923002678?via%3Dihub | es_ES |
dc.identifier.doi | https://doi.org/10.1016/j.chemolab.2023.105017 | - |
dc.identifier.publicationtitle | Chemometrics and Intelligent Laboratory Systems | es_ES |
dc.identifier.publicationfirstpage | 105017-1 | es_ES |
dc.identifier.publicationlastpage | 105017-14 | es_ES |
dc.identifier.publicationvolume | 243 | es_ES |
dc.identifier.orcid | 0000-0001-5993-2521 | es_ES |
dc.identifier.orcid | 0000-0002-9565-743X | es_ES |
dc.identifier.orcid | 0000-0002-3046-6368 | es_ES |
Colección: | DISIT - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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j_chemolab_2023_105017.pdf | 3,28 MB | Adobe PDF | Descargar |
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