Publicación:
Detección en tiempo real de fibrilación auricular en computador de placa reducida

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dc.contributor.authorMaya Gonzalez, Juan Carlosspa
dc.date.accessioned2022-06-01 00:00:00
dc.date.accessioned2022-06-17T20:21:34Z
dc.date.available2022-06-01 00:00:00
dc.date.available2022-06-17T20:21:34Z
dc.date.issued2022-06-01
dc.description.abstractEl desarrollo de dispositivos portables, que permita la detección en tiempo real de fibrilación auricular, requiere la implementación de algoritmos de reconocimiento automático de patrones con la metodología adecuada para su ejecución en sistemas embebidos. En el presente artículo se expone la implementación de una red neuronal artificial (ANN), una máquina de soporte vectorial (SVM) y un algoritmo de K vecinos más cercanos (KNN) en un computador de placa reducida para así comparar su desempeño en cuanto a la capacidad de detección de esta arritmia y el tiempo de respuesta asociado en su ejecución en tiempo real. La base de datos MIT-BIH AFIB es usada para el entrenamiento y validación de los algoritmos previa extracción de parámetros asociados a la transformada wavelet estacionaria. Se encontraron resultados entre el 92% y 97% para la sensibilidad y especificidad de los algoritmos mencionados y tiempos de respuesta variados entre 6 s y 7,1 sspa
dc.description.abstractDevelopment of portable devices, that allows real-time detection of atrial fibrillation, requires the implementation of automatic pattern recognition algorithms and an appropriate methodology for their execution in embedded systems. In the present article, the performances of an artificial neural network, a machine vector support, a k-nearest neighbors algorithm and a hybrid classifier implemented on a single-board computer, were compared in terms of detection capacity of arrhythmia and time response associated with real-time execution. The MIT-BIH AFIB database was used to train and validate the algorithms. In advance, the extraction of parameters associated with the stationary wavelet transform was developed. Results between 92 % and 97 % for sensitivity and specificity, and time responses between 6 s and 7.1 s were found in this research.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.24050/reia.v19i38.1565
dc.identifier.eissn2463-0950
dc.identifier.issn1794-1237
dc.identifier.urihttps://repository.eia.edu.co/handle/11190/5180
dc.identifier.urlhttps://doi.org/10.24050/reia.v19i38.1565
dc.language.isospaspa
dc.publisherFondo Editorial EIA - Universidad EIAspa
dc.relation.bitstreamhttps://revistas.eia.edu.co/index.php/reveia/article/download/1565/1475
dc.relation.citationeditionNúm. 38 , Año 2022 : .spa
dc.relation.citationendpage14
dc.relation.citationissue38spa
dc.relation.citationstartpage3823 pp. 1
dc.relation.citationvolume19spa
dc.relation.ispartofjournalRevista EIAspa
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dc.rightsRevista EIA - 2022spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
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dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.spa
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dc.sourcehttps://revistas.eia.edu.co/index.php/reveia/article/view/1565spa
dc.subjectAtrial fibrillationeng
dc.subjectartificial neural network (ANN)eng
dc.subjecthybrid classifiereng
dc.subjectk-nearest neighbors algorithm (KNN)eng
dc.subjectsingle-board computereng
dc.subjectsupport vector machine (SVM)eng
dc.subjectstatic wavelet transformeng
dc.subjectComputador de placa reducidaspa
dc.subjectFibrilación auricularspa
dc.subjectK vecinos más cercanos (KNN)spa
dc.subjectred neuronal artificial (ANN)spa
dc.subjectmáquina de soporte vectorial (SVM)spa
dc.subjectTransformada Wavelet Estacionariaspa
dc.titleDetección en tiempo real de fibrilación auricular en computador de placa reducidaspa
dc.title.translatedReal-time detection of atrial fibrillation on single board computereng
dc.typeArtículo de revistaspa
dc.typeJournal articleeng
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