Colombia | Artículo de revista
dc.creatorPeñuela Calderón, Lina María
dc.creatorCaicedo Gutierrez, Nicolas Esteban
dc.date.accessioned2022-06-01 00:00:00
dc.date.accessioned2022-06-17T20:21:38Z
dc.date.accessioned2022-09-29T14:49:36Z
dc.date.available2022-06-01 00:00:00
dc.date.available2022-06-17T20:21:38Z
dc.date.available2022-09-29T14:49:36Z
dc.date.created2022-06-01 00:00:00
dc.date.created2022-06-17T20:21:38Z
dc.date.issued2022-06-01
dc.identifier1794-1237
dc.identifierhttps://repository.eia.edu.co/handle/11190/5186
dc.identifier10.24050/reia.v19i38.1577
dc.identifier2463-0950
dc.identifierhttps://doi.org/10.24050/reia.v19i38.1577
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3777015
dc.description.abstractLa evaluación de dolor es de gran importancia en el campo de la medicina ya que permite detectar condiciones médicas o definir la manera en la que se debe tratar. Su evaluación se basa en primera instancia en información que el mismo paciente entrega. Sin embargo, en algunos casos en los que el paciente no tiene la capacidad de expresarlo, resulta de gran utilidad métodos que permitan evaluarlo. En este artículo se propone la evaluación de presencia o ausencia de dolor a partir de características asociadas a señales electro-encefalográficas en un experimento en el que se induce dolor agudo a 14 participantes con una prueba de electro-diagnóstico, en hombres y mujeres con edades entre 18 y 33 años.  Se utilizan redes neuronales para la clasificación, obteniendo una exactitud del 74,19 %.
dc.description.abstractThe evaluation of pain allows the detection of medical conditions and defines the procedure to treat them. Medical staff measures pain by patient´s self-report. Nevertheless, in some cases, it is difficult or impossible for the patient to communicate the level of pain perceived. In these cases, it is useful to evaluate pain employing different techniques. In this paper, we propose the evaluation of pain through a procedure based on the analysis of the electroencephalographic signals. The algorithms were evaluated in an experiment with 14 participants where the pain was induced with an electrodiagnostic system. The participants were males and females between 18 and 33 years old. To classify between pain and no pain, we employed neural networks with an accuracy of 74,19 %.  
dc.languagespa
dc.publisherFondo Editorial EIA - Universidad EIA
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dc.relationhttps://revistas.eia.edu.co/index.php/reveia/article/download/1577/1482
dc.relationNúm. 38 , Año 2022 : .
dc.relation18
dc.relation38
dc.relation3829 pp. 1
dc.relation19
dc.relationRevista EIA
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsRevista EIA - 2022
dc.sourcehttps://revistas.eia.edu.co/index.php/reveia/article/view/1577
dc.subjectRedes Neuronales
dc.subjectElectroencefalografía
dc.subjectDensidad del Espectro de Frecuencia
dc.subjectValor Medio Cuadrático
dc.subjectFrecuencia Pico
dc.subjectEscala Análoga Visual
dc.subjectEscala de Valoración Numérica
dc.subjectElectro-diagnóstico
dc.subjectNeural Networks
dc.subjectElectroencephalography
dc.subjectPower Spectral Density
dc.subjectRoot-Mean-Square
dc.subjectPeak Frequency
dc.subjectVisual Analog Scale
dc.subjectNumerical Rating Scale
dc.subjectElectrodiagnosis
dc.titleDetección de dolor apartir de señales de EEG
dc.typeArtículo de revista
dc.typeJournal article


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