Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/73728
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dc.contributor.authorMostafa, Sheikh Shanawazen_US
dc.contributor.authorBaptista, Daríoen_US
dc.contributor.authorRavelo García, Antonio Gabrielen_US
dc.contributor.authorJuliá-Serdá, Gabrielen_US
dc.contributor.authorMorgado-Dias, Fernandoen_US
dc.date.accessioned2020-07-20T08:34:49Z-
dc.date.available2020-07-20T08:34:49Z-
dc.date.issued2020en_US
dc.identifier.issn0169-2607en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/73728-
dc.description.abstractBackground and objective: Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test called polysomnography. A single SpO2 sensor based on an automated classification system can be developed to simplify the apnea detection. The main objective of this work is to develop a classifier based on a convolution neural network with the capability of detecting apnea events from one dimensional SpO2 signal. However, to find an optimum convolution neural network structure is a daunting task is usually done by a trial-and-error method. To solve this problem, a method is proposed to save time and simplify the process of searching for an optimum convolution neural network structure. Methods: Greedy based optimization is proposed to search for an optimized convolution neural network structure. Three different variants of greedy based optimization are proposed: the topology transfer, the weighted-topology transfer with rough estimation, and the weighted-topology transfer with fine tuning. The subject independent and the cross-database test are performed for the analysis. Results: Considering the balance between the execution time and the performance, the weighted-topology transfer with rough estimation is the best. An accuracy of 88.49% for the HuGCDN2008 database and 95.14% for the Apnea-ECG database are obtained for apnea events detection per minute. Regarding the apnea patient detection, also referred to as global classification, an accuracy of 95.71% is achieved for the HuGCDN2008 database, and 100% is achieved for the AED database without removing any subjects from both databases. Conclusions: The proposed one-dimensional convolution neural network performs better in a similar situation than those presented in the literature. The greedy based methods, mainly the weighted-topology transfer with rough estimation, is an alternative method to extensive trial and error method.en_US
dc.languageengen_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.sourceComputer Methods and Programs in Biomedicine [ISSN 0169-2607], n. 197en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherClassification Algorithms, Sleep Apneaen_US
dc.subject.otherCnnen_US
dc.subject.otherHyperparameteren_US
dc.subject.otherOptimizationen_US
dc.titleGreedy based convolutional neural network optimization for detecting apneaen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.cmpb.2020.105640en_US
dc.identifier.scopus85087783031-
dc.contributor.authorscopusid55489640900-
dc.contributor.authorscopusid42360968300-
dc.contributor.authorscopusid9634135600-
dc.contributor.authorscopusid6603171553-
dc.contributor.authorscopusid57200602527-
dc.identifier.eissn1872-7565-
dc.relation.volume197en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.date.coverdateDiciembre 2020en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr0,924
dc.description.jcr5,428
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-8512-965X-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameRavelo García, Antonio Gabriel-
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