<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-6266</journal-id>
<journal-title><![CDATA[Acta universitaria]]></journal-title>
<abbrev-journal-title><![CDATA[Acta univ]]></abbrev-journal-title>
<issn>0188-6266</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Guanajuato, Dirección de Investigación y Posgrado]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-62662019000100104</article-id>
<article-id pub-id-type="doi">10.15174/au.2019.1672</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis de electroencefalograma usando redes neuronales artificiales]]></article-title>
<article-title xml:lang="en"><![CDATA[Electroencephalogram analysis using artificial neural networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Delgado]]></surname>
<given-names><![CDATA[Karina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ledesma]]></surname>
<given-names><![CDATA[Sergio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rostro]]></surname>
<given-names><![CDATA[Horacio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Guanajuato División de Ingenierías Departamento de Ingeniería Electrónica]]></institution>
<addr-line><![CDATA[Salamanca Gto]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<volume>29</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-62662019000100104&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-62662019000100104&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-62662019000100104&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen A través de la electroencefalografía se detecta la comunicación entre señales eléctricas creadas por las neuronas que, al conectarse entre sí, crean conexiones sinápticas. Esta técnica ha sido muy importante en la detección de trastornos neurológicos como la epilepsia. Caracterizada por cambios temporales en el funcionamiento bioeléctrico del cerebro, la epilepsia provoca convulsiones que afectan a la conciencia, el movimiento o la sensibilidad. Las redes neuronales artificiales (RNA) proporcionan modelos con diversas alternativas para detección, clasificación y predicción de muestras mediante el análisis del electroencefalograma a partir de la estructura de los datos, los cuales determinan la topología de la red. Este artículo propone la implementación de un modelo basado en RNA para analizar, clasificar y procesar señales epilépticas a partir del entrenamiento. Particularmente, la base de datos cuenta con muestras que registraron la actividad cerebral de pacientes sanos, pacientes que controlaron las crisis y pacientes que aún registraban oscilaciones en las señales emitidas por la actividad cerebral. Después de aplicar la transformada de Fourier, estas señales se integraron en una matriz aplicando tres tipos de umbral, procediendo a seleccionar los datos de entrada de la RNA para su entrenamiento y validación. Se consideran dos métodos de aprendizaje: redes neuronales multicapa con validación clásica (back propagation) y redes neuronales con validación cruzada (LOOCV, por sus siglas en inglés), para ello se calcula el error cuadrático medio (MSE, por sus siglas en inglés) así como la cantidad de errores por umbral con la finalidad de comparar los resultados obtenidos y precisar el método que proporciona los mejores resultados. Ambas redes se entrenaron usando un método híbrido basado en el templado simulado y el gradiente conjugado. Finalmente, se presenta el análisis de las RNA como sistemas de clasificación a través de los dos métodos en funcionamiento, alcanzando resultados satisfactorios que manifiestan la aplicación como herramienta de apoyo al diagnóstico médico para la detección de este trastorno.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Through electroencephalography it is possible to detect the electrical signals created by interconnected neurons that create synaptic connections. This technique has been very important in the detection of neurological disorders such as epilepsy. Characterized by temporary changes in the bioelectric function of the brain, epilepsy causes seizures that affect awareness, movement, or sensation. Artificial neural networks (ANN) provide alternative models for detection, classification and prediction of samples by analyzing the electroencephalogram from the structure of the data, which determine the topology of the network. This article proposes the implementation of a system based on ANN to analyze, classify and process signals from an epileptic training model. In particular, the database has samples with recorded brain activity in healthy patients, patients who controlled the crisis and patients that still recorded oscillations in the signals emitted by the brain activity. After applying the fast Fourier transform, these signals were integrated into a matrix using three types of threshold and selecting the input data of an ANN for training and validation. Two methods of learning are considered: multilayer neural networks with classic validation (back propagation) and neural networks using leave one out crossed validation (LOOCV), for which the mean square error (MSE) and the amount of errors threshold are calculated in order to compare the results obtained and to find the method that provides the best results. Both networks were trained using a hybrid method based on simulated annealing and conjugate gradient. Finally, the analysis of ANN as classification systems through the two methods in operation is presented, achieving satisfactory results that show the application as a tool to support the medical diagnosis for the detection of this disorder.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[red neuronal artificial]]></kwd>
<kwd lng="es"><![CDATA[electroencefalograma]]></kwd>
<kwd lng="es"><![CDATA[error cuadrático medio]]></kwd>
<kwd lng="es"><![CDATA[validación cruzada]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[electroencephalogram]]></kwd>
<kwd lng="en"><![CDATA[mean squared error]]></kwd>
<kwd lng="en"><![CDATA[cross validation]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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