<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462021000200269</article-id>
<article-id pub-id-type="doi">10.13053/cys-25-2-3461</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Detección de señales EEG epilépticas utilizando redes convolucionales basada en la transformada synchrosqueezing acolchada]]></article-title>
<article-title xml:lang="en"><![CDATA[Epileptic Signal Detection Using Quilted Synchrosqueezing Transform Based Convolutional Neural Networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villazana]]></surname>
<given-names><![CDATA[Sergio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Montilla]]></surname>
<given-names><![CDATA[Guillermo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Eblen]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Maldonado]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Carabobo Centro de Procesamiento de Imágenes ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Venezuela</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Yttrium-Technology Corp  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Panama</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Diego Portales Facultad de Medicina Laboratorio de Neurociencia Translacional]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Chile</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>25</volume>
<numero>2</numero>
<fpage>269</fpage>
<lpage>286</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462021000200269&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462021000200269&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462021000200269&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este trabajo propone un algoritmo basado en redes neuronales convolucionales para clasificar señales electroencefalográficas (EEG) en las clases normal, preictal e ictal, como apoyo para el especialista médico para facilitar el diagnóstico de la condición de epilepsia. Las señales EEG se pre-procesan mediante la aplicación de la transformada synchrosqueezing basada en la trasformada corta de Fourier acolchada (SS-QSTFT de sus siglas en ingles), que genera como salida una representación tiempo-frecuencia que se utiliza como entrada a la red neuronal convolucional. El entrenamiento de los clasificadores se realizan con los registros de la base de datos EEG de la Universidad de Bonn, compuesta de cinco conjuntos identificados como A, B, C, D y E. Las clases normal, preictal e ictal se formaron con los conjuntos A-B, C-D y E, respectivamente. La exactitud, sensibilidad y especificidad del mejor modelo clasificador CNN obtenido fueron de 99,61; 99,10 y 98,99, respectivamente. Adicionalmente, se desarrolló otro clasificador basado en las máquinas de vectores de soporte (SVM de sus siglas en inglés) utilizando como extractor de rasgos el modelo CNN entrenado, al cual se le elimino la capa de salida. Los rasgos de entrada a la SVM se tomaron de la salida de la capa densamente conectada de la CNN. La SVM se entrenó con los mismos datos (representación tiempo-frecuencia de las señales) con los que se entrenó la CNN, y su desempeño en exactitud, sensibilidad y especificidad fue del 100%, tanto para los datos de entrenamiento como para los datos de prueba.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This work proposes a convolutional neural networks-based algorithm to classify electroencephalo-graphic signals (EEG) in normal, preictal and ictal classes to supporting to the physicists to diagnose the epilepsy condición. EEG signals are preprocessed through the application of the synchrosqueezing transform based on the quilted short time Fourier transform (SS-QSTFT) to generate a time-frequency representation, which is the input to the convolutional neural network (CNN). CNN based classifiers are trained using the EEG database of the University of Bonn, which have five sets identified as A, B, C, D and E. Normal, preictal and ictal classes were composed with the combinación of the sets A-B, C-D and E, respectively. Accuracy, sensitivity and specificity of the best CNN-based classifier were 99.61, 99.10 and 98.99, respectively. Furthermore, another support vector machines (SVM)-based classifier was developed using the previous CNN model as feature extractor, which last output layer was removed. Input features to the SVM were taken from the fully-connected layer of the CNN. SVM were trained using the same data (time-frequency representation) utilized to train the previous CNN, and their performance in accuracy, sensitivity and specificity were 100% for training and testing sets.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Señales EEG epilépticas]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales convolucionales]]></kwd>
<kwd lng="es"><![CDATA[SST-QSTFT]]></kwd>
<kwd lng="en"><![CDATA[Epileptic EEG signals]]></kwd>
<kwd lng="en"><![CDATA[convolutional neural networks]]></kwd>
<kwd lng="en"><![CDATA[SST-QSTFT]]></kwd>
</kwd-group>
</article-meta>
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