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Detección de dolor apartir de señales de EEG

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dc.contributor.authorPeñuela Calderón, Lina Maríaspa
dc.contributor.authorCaicedo Gutierrez, Nicolas Estebanspa
dc.date.accessioned2022-06-01 00:00:00
dc.date.accessioned2022-06-17T20:21:38Z
dc.date.available2022-06-01 00:00:00
dc.date.available2022-06-17T20:21:38Z
dc.date.issued2022-06-01
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 %.spa
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 %.  eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.24050/reia.v19i38.1577
dc.identifier.eissn2463-0950
dc.identifier.issn1794-1237
dc.identifier.urihttps://repository.eia.edu.co/handle/11190/5186
dc.identifier.urlhttps://doi.org/10.24050/reia.v19i38.1577
dc.language.isospaspa
dc.publisherFondo Editorial EIA - Universidad EIAspa
dc.relation.bitstreamhttps://revistas.eia.edu.co/index.php/reveia/article/download/1577/1482
dc.relation.citationeditionNúm. 38 , Año 2022 : .spa
dc.relation.citationendpage18
dc.relation.citationissue38spa
dc.relation.citationstartpage3829 pp. 1
dc.relation.citationvolume19spa
dc.relation.ispartofjournalRevista EIAspa
dc.relation.referencesGrabowski, S. R., & Tortora, G. J. (2000). Principles of anatomy and physiology. New York/Chichester: Wiley.spa
dc.relation.referencesTortora, G. J., & Nielsen, M. T. (1995). Human Anatomy. Harper Collins College Pub. Ilana, E. (1979). Pain terms; a list with definitions and notes on usage. Pain, 6, 249.spa
dc.relation.referencesManworren, R. C., & Stinson, J. (2016). Pediatric pain measurement, assessment, and evaluation. In Seminars in pediatric neurology (Vol. 23, No. 3, pp. 189-200). WB Saunders. DOI: https: / / doi .org /10.1016/j.spen.2016.10.001.spa
dc.relation.referencesKagita, J., & Mitsukura, Y. (2018). Quantification of pain degree by frequency features of single-chanelled EEG. In 2018 IEEE 15th International Workshop on Advanced Motion Control (AMC) (pp. 359-363). IEEE.spa
dc.relation.referencesTatum IV, W. O. (2021). Handbook of EEG interpretation. Springer Publishing Company. Bonotis, P. A., Tsouros, D. C., Smyrlis, P. N., Tzallas, A. T., Giannakeas, N., Glavas, E., & Tsipouras, M. G. (2019). Automated Assessment of Pain Intensity based on EEG Signal Analysis. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 583-588). IEEE.spa
dc.relation.referencesPanavaranan, P., & Wongsawat, Y. (2013). EEG-based pain estimation via fuzzy logic and polynomial kernel support vector machine. In The 6th 2013 Biomedical Engineering International Conference (pp. 1-4). IEEE. Nir, R. R., Sinai, A., Raz, E., Sprecher, E., & Yarnitsky, D. (2010). Pain assessment by continuous EEG: association between subjective perception of tonic pain and peak frequency of alpha oscillations during stimulation and at rest. Brain research, 1344, 77-86.spa
dc.relation.referencesCao, T., Wang, Q., Liu, D., Sun, J., & Bai, O. (2020). Resting state EEG-based sudden pain recognition method and experimental study. Biomedical Signal Processing and Control, 59, 101925.spa
dc.relation.referencesTatum IV, W. O. (2021). Handbook of EEG interpretation. Springer Publishing Company.spa
dc.relation.referencesBai, Y., Hu, Y., & Zhang, Z. (2016). Spontaneous EEG-based normalization of pain-evoked neural responses: Effect on improving the accuracy of pain prediction. In 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (pp. 1-4). IEEE.spa
dc.relation.referencesYu, M., Sun, Y., Zhu, B., Zhu, L., Lin, Y., Tang, X., ... & Dong, M. (2020). Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG. Neurocomputing, 378, 270-282.spa
dc.rightsRevista EIA - 2022spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0spa
dc.sourcehttps://revistas.eia.edu.co/index.php/reveia/article/view/1577spa
dc.subjectRedes Neuronalesspa
dc.subjectElectroencefalografíaspa
dc.subjectDensidad del Espectro de Frecuenciaspa
dc.subjectValor Medio Cuadráticospa
dc.subjectFrecuencia Picospa
dc.subjectEscala Análoga Visualspa
dc.subjectEscala de Valoración Numéricaspa
dc.subjectElectro-diagnósticospa
dc.subjectNeural Networkseng
dc.subjectElectroencephalographyeng
dc.subjectPower Spectral Densityeng
dc.subjectRoot-Mean-Squareeng
dc.subjectPeak Frequencyeng
dc.subjectVisual Analog Scaleeng
dc.subjectNumerical Rating Scaleeng
dc.subjectElectrodiagnosiseng
dc.titleDetección de dolor apartir de señales de EEGspa
dc.title.translatedPain detection evaluated from electroencephalographic signalseng
dc.typeArtículo de revistaspa
dc.typeJournal articleeng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTREFspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
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