Show simple item record

dc.contributor.authorMartínez-Toro, Gabriel Mauricio
dc.contributor.authorRico-Bautista, Dewar
dc.contributor.authorRomero-Riaño, Efrén
dc.contributor.authorRomero-Riaño, Paola Andrea
dc.date.accessioned2020-10-27T00:19:55Z
dc.date.available2020-10-27T00:19:55Z
dc.date.issued2019-12-01
dc.identifier.issn2539-2115
dc.identifier.issn1657-2831
dc.identifier.urihttp://hdl.handle.net/20.500.12749/8823
dc.description.abstractLa epilepsia es uno de los trastornos neurológicos comunes caracterizado por convulsiones recurrentes. El objetivo principal de este artículo es dar a conocer el análisis de los resultados presentados en las gráficas de simulación de los datos de entrenamiento. Los datos fueron recolectados mediante el sistema 10-20. El sistema "10-20" es un método reconocido internacionalmente, este describe la ubicación de electrodos en la cabeza para una prueba de EEG. Se muestran las diferencias obtenidas entre las pruebas generadas con las anomalías de los datos de prueba a partir de los datos de entrenamiento. Finalmente, se interpretan los resultados y se discute sobre la eficacia del procedimiento.
dc.format.mimetypeapplication/pdf
dc.format.mimetypeText/html
dc.language.isoeng
dc.publisherUniversidad Autónoma de Bucaramanga UNAB
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/3718/3155
dc.relationHttps://revistas.unab.edu.co/index.php/rcc/article/view/3718/3141
dc.relation/*ref*/Aarabi, A., & He, B. (2012). A rule-based seizure prediction method for focal neocortical epilepsy. Clinical Neurophysiology, 123(6), 1111–1122. https://doi.org/10.1016/j.clinph.2012.01.014
dc.relation/*ref*/Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., Zhavoronkov, A., & Albuquerque, N. (2016). HHS Public Access, 13(7), 2524–2530. https://doi.org/10.1021/acs.molpharmaceut.6b00248.Deep
dc.relation/*ref*/Alshebeili, S. A., Alshawi, T., Ahmad, I., & El-samie, F. E. A. (2014). EEG seizure detection and prediction algorithms : a survey. EURASIP Journal on Advances in Signal Processing, 183(1), 1,21. https://doi.org/10.1186/1687-6180-2014-183
dc.relation/*ref*/Beatriz Pérez Salazar, Á., & Lillia Hernández López, D. (2007). Epilepsia: aspectos básicos para la práctica psiquiátrica Epilepsia: aspectos básicos para la práctica psiquiátrica Title: Epilepsy: Basic Aspects for the Practice of Psychiatry. Rev. Colomb. Psiquiat, XXXVI XXXV(1), 175–186.
dc.relation/*ref*/Chang, C.-C., & Lin, C.-J. (2011). Libsvm. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27. https://doi.org/10.1145/1961189.1961199
dc.relation/*ref*/Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., & Fuggetta, F. (2010). Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines. IEEE Transactions on Biomedical Engineering, 57(5), 1124–1132. https://doi.org/10.1109/TBME.2009.2038990
dc.relation/*ref*/Cruces, H. De. (2014). Tipos de crisis epilépticas y pseudocrisis Diferencial characteristics of epileptic seizure and pseudoseizures, 105–107.
dc.relation/*ref*/Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics, 35(5–6), 352–359. https://doi.org/10.1016/S1532-0464(03)00034-0
dc.relation/*ref*/Dudoit, S., Fridlyand, J., & Speed, T. P. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97(457), 77–86. https://doi.org/10.1198/016214502753479248
dc.relation/*ref*/Escalona-Morán, M., Cosenza, M. G., Guillén, P., & Coutin, P. (2007). Synchronization and clustering in electroencephalographic signals. Chaos, Solitons and Fractals, 31(4), 820–825. https://doi.org/10.1016/j.chaos.2005.10.049
dc.relation/*ref*/Fuertes, B., López, R., & Gil, P. (2007). Epilepsia. Tratado de Geriatria Para Residentes, 519–530.
dc.relation/*ref*/Garg, S., & Narvey, R. (2013). Denoising & feature extraction of eeg signal using wavelet transform. International Journal of Engineering Science and Technology., 5(06), 1249–1253.
dc.relation/*ref*/Griffis, J. C., Allendorfer, J. B., & Szaflarski, J. P. (2016). Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. Journal of Neuroscience Methods, 257, 97–108. https://doi.org/10.1016/j.jneumeth.2015.09.019
dc.relation/*ref*/Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47–56. https://doi.org/10.1016/j.neucom.2013.03.047
dc.relation/*ref*/Kurzynski, M., Krysmann, M., Trajdos, P., & Wolczowski, A. (2016). Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand. Computers in Biology and Medicine, 69, 286–297. https://doi.org/10.1016/j.compbiomed.2015.04.023
dc.relation/*ref*/Langkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42(1), 11–24. https://doi.org/10.1016/j.patrec.2014.01.008
dc.relation/*ref*/López-meraz, M. L., Rocha, L., Miquel, M., Hernández, M. E., Cárdenas, R. T., Coria-ávila, G. A., … Manzo, J. (2009). Conceptos básicos de la epilepsia. Revista Medica de La Universidad Veracruzana, 9(2), 31–37.
dc.relation/*ref*/Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R. (2009). Classification of patterns of EEG synchronization for seizure prediction. Clinical Neurophysiology, 120(11), 1927–1940. https://doi.org/10.1016/j.clinph.2009.09.002
dc.relation/*ref*/Mirowski, P. W., Lecun, Y., Madhavan, D., & Kuzniecky, R. (2008). Comparing SVM and Convolutional Networks for Epileptic Seizure.
dc.relation/*ref*/Mirowski, P. W., Madhavan, D., & Lecun, Y. (2007). Time-delay neural networks and independent component analysis for eeg-based prediction of epileptic seizures propagation. Advancement of Artificial Intelligence Conference, 1892–1893.
dc.relation/*ref*/Soleimani-B., H., Lucas, C., N. Araabi, B., & Schwabe, L. (2012). Adaptive prediction of epileptic seizures from intracranial recordings. Biomedical Signal Processing and Control, 7(5), 456–464. https://doi.org/10.1016/j.bspc.2011.11.007
dc.relation/*ref*/Tsai, C. F. (2014). Combining cluster analysis with classifier ensembles to predict financial distress. ACM Transactions on Intelligent Systems and Technology, 16(1), 46–58. https://doi.org/10.1016/j.inffus.2011.12.001
dc.relation/*ref*/Valencia, J. F., Melia, U. S. P., Vallverdú, M., Borrat, X., Jospin, M., Jensen, E. W., … Caminal, P. (2016). Assessment of nociceptive responsiveness levels during sedation-analgesia by entropy analysis of EEG. Entropy, 18(3). https://doi.org/10.3390/e18030103
dc.relation/*ref*/Wang, D., & Shang, Y. (2014). Modeling Physiological Data with Deep Belief Networks. International Journal of Education Technology, 3(5), 505–511. https://doi.org/10.7763/IJIET.2013.V3.326.Modeling
dc.relation/*ref*/Wulsin, D., Blanco, J., Mani, R., & Litt, B. (2010). Semi-supervised anomaly detection for EEG waveforms using deep belief nets. Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010, (April 2016), 436–441. https://doi.org/10.1109/ICMLA.2010.71
dc.relation/*ref*/Wulsin, D. F., Gupta, J. R., Mani, R., Blanco, J. A., & Litt, B. (2011). Modeling electroencephalography waveforms with semi-supervised deep belief nets: Fast classification and anomaly measurement. Journal of Neural Engineering, 8(3). https://doi.org/10.1088/1741-2560/8/3/036015
dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/article/view/3718
dc.rightsDerechos de autor 2019 Revista Colombiana de Computación
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceRevista Colombiana de Computación; Vol. 20 Núm. 2 (2019): Revista Colombiana de Computación; 20-27
dc.subjectEpilepsia
dc.subjectAprendizaje profundo
dc.subjectAprendizaje automático
dc.subjectAuto codificación
dc.titleAprendizaje no supervisado: aplicación en epilepsia
dc.title.translatedUnsupervised learning: application to epilepsy
dc.type.driverinfo:eu-repo/semantics/article
dc.type.localArtículospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsEpilepsy
dc.subject.keywordsDeep learning
dc.subject.keywordsAutomatic learning
dc.subject.keywordsAuto-encoding
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNAB
dc.type.hasversionInfo:eu-repo/semantics/publishedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.identifier.repourlrepourl:https://repositorio.unbosque.edu.co
dc.description.abstractenglishEpilepsy is a neurological disorder characterized by recurrent seizures. The primary objective is to present an analysis of the results shown in the training data simulation charts. Data were collected by means of the 10-20 system. The “10–20” system is an internationally recognized method to describe and apply the location of scalp electrodes in the context of an EEG exam. It shows the differences obtained between the tests generated and the anomalies of the test data based on training data. Finally, the results are interpreted and the efficacy of the procedure is discussed.
dc.identifier.doi10.29375/25392115.3718
dc.type.redcolhttp://purl.org/redcol/resource_type/CJournalArticle
dc.rights.creativecommonsAttribution-NonCommercial-ShareAlike 4.0 International*


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International