Aprendizaje no supervisado: aplicación en epilepsia

La 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...

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Autores:: Martínez Toro, Gabriel Mauricio
Rico Bautista, Dewar
Romero Riaño, Efrén
Romero Riaño, Paola Andrea

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2019

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: eng

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repository_id_str
dc.title.none.fl_str_mv	Aprendizaje no supervisado: aplicación en epilepsia
dc.title.translated.none.fl_str_mv	Unsupervised learning: application to epilepsy
title	Aprendizaje no supervisado: aplicación en epilepsia
spellingShingle	Aprendizaje no supervisado: aplicación en epilepsia Epilepsy Deep learning Automatic learning Auto-encoding Ciencia de los computadores Investigación Epilepsy Deep learning Automatic learning Auto-encoding
title_short	Aprendizaje no supervisado: aplicación en epilepsia
title_full	Aprendizaje no supervisado: aplicación en epilepsia
title_fullStr	Aprendizaje no supervisado: aplicación en epilepsia
title_full_unstemmed	Aprendizaje no supervisado: aplicación en epilepsia
title_sort	Aprendizaje no supervisado: aplicación en epilepsia
dc.creator.fl_str_mv	Martínez Toro, Gabriel Mauricio Rico Bautista, Dewar Romero Riaño, Efrén Romero Riaño, Paola Andrea
dc.contributor.author.spa.fl_str_mv	Martínez Toro, Gabriel Mauricio Rico Bautista, Dewar Romero Riaño, Efrén Romero Riaño, Paola Andrea
dc.contributor.cvlac.none.fl_str_mv	Martínez Toro, Gabriel Mauricio [0001489133]
dc.contributor.googlescholar.none.fl_str_mv	Martínez Toro, Gabriel Mauricio [NKXdCogAAAAJ] Rico Bautista, Dewar [q_ZtKjsAAAAJ&hl=es&oi=ao] Romero Riaño, Efrén [iduK4zEAAAAJ&hl=es&oi=ao]
dc.contributor.orcid.none.fl_str_mv	Rico Bautista, Dewar [0000-0002-1808-3874] Romero Riaño, Efrén [0000-0002-3627-9942]
dc.contributor.scopus.none.fl_str_mv	Martínez Toro, Gabriel Mauricio [57205705742]
dc.subject.keywords.eng.fl_str_mv	Epilepsy Deep learning Automatic learning Auto-encoding
topic	Epilepsy Deep learning Automatic learning Auto-encoding Ciencia de los computadores Investigación Epilepsy Deep learning Automatic learning Auto-encoding
dc.subject.lemb.none.fl_str_mv	Ciencia de los computadores Investigación
dc.subject.proposal.none.fl_str_mv	Epilepsy Deep learning Automatic learning Auto-encoding
description	La 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.
publishDate	2019
dc.date.issued.none.fl_str_mv	2019-12-01
dc.date.accessioned.none.fl_str_mv	2020-10-27T00:19:55Z
dc.date.available.none.fl_str_mv	2020-10-27T00:19:55Z
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dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.type.local.spa.fl_str_mv	Artículo
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dc.identifier.issn.none.fl_str_mv	2539-2115 1657-2831
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/20.500.12749/8823
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Autónoma de Bucaramanga UNAB
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dc.identifier.doi.none.fl_str_mv	10.29375/25392115.3718
identifier_str_mv	2539-2115 1657-2831 instname:Universidad Autónoma de Bucaramanga UNAB repourl:https://repository.unab.edu.co 10.29375/25392115.3718
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dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.none.fl_str_mv	https://revistas.unab.edu.co/index.php/rcc/article/view/3718/3155 Https://revistas.unab.edu.co/index.php/rcc/article/view/3718/3141
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dc.relation.references.none.fl_str_mv	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 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 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 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 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 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 Cruces, H. De. (2014). Tipos de crisis epilépticas y pseudocrisis Diferencial characteristics of epileptic seizure and pseudoseizures, 105–107 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 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 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 Fuertes, B., López, R., & Gil, P. (2007). Epilepsia. Tratado de Geriatria Para Residentes, 519–530. 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 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 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 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 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 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 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 Mirowski, P. W., Lecun, Y., Madhavan, D., & Kuzniecky, R. (2008). Comparing SVM and Convolutional Networks for Epileptic Seizure 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 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 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 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 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 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 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.rights.none.fl_str_mv	Derechos de autor 2019 Revista Colombiana de Computación
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publisher.none.fl_str_mv	Universidad Autónoma de Bucaramanga UNAB
dc.source.none.fl_str_mv	Revista Colombiana de Computación; Vol. 20 Núm. 2 (2019): Revista Colombiana de Computación; 20-27
institution	Universidad Autónoma de Bucaramanga - UNAB
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spelling	Martínez Toro, Gabriel Mauricioc3555eb3-ccab-4132-b8d5-f0badaad090bRico Bautista, Dewar19c5d768-34d2-47cc-8059-f1ab8df09ca7Romero Riaño, Efrén76106587-6d1f-4174-87cd-258f1540ce74Romero Riaño, Paola Andreaf1439795-c79c-4524-b92c-f04ba675d184Martínez Toro, Gabriel Mauricio [0001489133]Martínez Toro, Gabriel Mauricio [NKXdCogAAAAJ]Rico Bautista, Dewar [q_ZtKjsAAAAJ&hl=es&oi=ao]Romero Riaño, Efrén [iduK4zEAAAAJ&hl=es&oi=ao]Rico Bautista, Dewar [0000-0002-1808-3874]Romero Riaño, Efrén [0000-0002-3627-9942]Martínez Toro, Gabriel Mauricio [57205705742]2020-10-27T00:19:55Z2020-10-27T00:19:55Z2019-12-012539-21151657-2831http://hdl.handle.net/20.500.12749/8823instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.co10.29375/25392115.3718La 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.Epilepsy 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.application/pdfText/htmlengUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería de Sistemashttps://revistas.unab.edu.co/index.php/rcc/article/view/3718/3155Https://revistas.unab.edu.co/index.php/rcc/article/view/3718/3141https://revistas.unab.edu.co/index.php/rcc/article/view/3718Aarabi, 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.2012.01.014Aliper, 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.DeepAlshebeili, 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/10.1186/1687-6180-2014-183Beatriz 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), 175–186Chang, C.-C., & Lin, C.-J. (2011). Libsvm. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27. https://doi.org/10.1145/1961189.1961199Chisci, 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.2009.2038990Cruces, H. De. (2014). Tipos de crisis epilépticas y pseudocrisis Diferencial characteristics of epileptic seizure and pseudoseizures, 105–107Dreiseitl, 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)00034-0Dudoit, 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/10.1198/016214502753479248Escalona-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.2005.10.049Fuertes, B., López, R., & Gil, P. (2007). Epilepsia. Tratado de Geriatria Para Residentes, 519–530.Garg, S., & Narvey, R. (2013). Denoising & feature extraction of eeg signal using wavelet transform. International Journal of Engineering Science and Technology., 5(06), 1249–1253Griffis, 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.2015.09.019Kuremoto, 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.2013.03.047Kurzynski, 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.2015.04.023Langkvist, 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.2014.01.008López-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), 31–37Mirowski, 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.2009.09.002Mirowski, P. W., Lecun, Y., Madhavan, D., & Kuzniecky, R. (2008). Comparing SVM and Convolutional Networks for Epileptic SeizureMirowski, 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, 1892–1893Soleimani-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.2011.11.007Tsai, 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.2011.12.001Valencia, 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/e18030103Wang, 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.ModelingWulsin, 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.2010.71Wulsin, 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/10.1088/1741-2560/8/3/036015Derechos de autor 2019 Revista Colombiana de Computaciónhttp://creativecommons.org/licenses/by-nc-sa/4.0/http://creativecommons.org/licenses/by-nc-nd/2.5/co/http://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Revista Colombiana de Computación; Vol. 20 Núm. 2 (2019): Revista Colombiana de Computación; 20-27Aprendizaje no supervisado: aplicación en epilepsiaUnsupervised learning: application to epilepsyinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/CJournalArticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85EpilepsyDeep learningAutomatic learningAuto-encodingCiencia de los computadoresInvestigaciónEpilepsyDeep learningAutomatic learningAuto-encodingORIGINAL2019_Aprendizaje_no_supervisado.pdf2019_Aprendizaje_no_supervisado.pdfArticuloapplication/pdf609865https://repository.unab.edu.co/bitstream/20.500.12749/8823/1/2019_Aprendizaje_no_supervisado.pdfabf47495fd5934aa71a843277d98c351MD51open accessTHUMBNAIL2019_Aprendizaje_no_supervisado.pdf.jpg2019_Aprendizaje_no_supervisado.pdf.jpgIM Thumbnailimage/jpeg12692https://repository.unab.edu.co/bitstream/20.500.12749/8823/2/2019_Aprendizaje_no_supervisado.pdf.jpg30f6c81cc4f98f78b6025f117fea6f07MD52open access20.500.12749/8823oai:repository.unab.edu.co:20.500.12749/88232022-11-28 17:32:00.446open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.co

Aprendizaje no supervisado: aplicación en epilepsia

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