Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/71930
Campo DC Valoridioma
dc.contributor.authorMendonca, Fabioen_US
dc.contributor.authorMostafa, Sheikh Shanawazen_US
dc.contributor.authorMorgado-Dias, Fernandoen_US
dc.contributor.authorRavelo-Garcia, Antonio G.en_US
dc.date.accessioned2020-05-04T08:57:11Z-
dc.date.available2020-05-04T08:57:11Z-
dc.date.issued2019en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/71930-
dc.description.abstractQuality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase's classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts' expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.en_US
dc.languageengen_US
dc.relation.ispartofEntropyen_US
dc.sourceEntropy [ISSN 1099-4300], v. 21 (12), 1203, (Diciembre 2019)en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherAutomatic Methoden_US
dc.subject.otherSleepen_US
dc.subject.otherCapen_US
dc.subject.otherClassificationen_US
dc.subject.otherSleep Qualityen_US
dc.subject.otherEegen_US
dc.subject.otherCapen_US
dc.subject.otherGruen_US
dc.subject.otherLstmen_US
dc.subject.other1D-Cnnen_US
dc.titleA Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivationen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/e21121203en_US
dc.identifier.isi000507376900001-
dc.identifier.eissn1099-4300-
dc.identifier.issue12-
dc.relation.volume21en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid6442981-
dc.contributor.daisngid4069296-
dc.contributor.daisngid1189663-
dc.contributor.daisngid1986395-
dc.description.numberofpages17en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Mendonca, F-
dc.contributor.wosstandardWOS:Mostafa, SS-
dc.contributor.wosstandardWOS:Morgado-Dias, F-
dc.contributor.wosstandardWOS:Ravelo-Garcia, AG-
dc.identifier.ulpgces
dc.description.sjr0,527
dc.description.jcr2,494
dc.description.sjrqQ2
dc.description.jcrqQ2
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
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-
Colección:Artículos
miniatura
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