<?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>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282019000300016</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Propuesta para la monitorización de estados de sedación en señales electroencefalográficas]]></article-title>
<article-title xml:lang="en"><![CDATA[Proposal for the monitoring of sedation states in electroencephalographic signals]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Rubio]]></surname>
<given-names><![CDATA[Tahimy]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Drullet Ferrer]]></surname>
<given-names><![CDATA[Jorge Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez Aldana]]></surname>
<given-names><![CDATA[Yissel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marañón Reyes]]></surname>
<given-names><![CDATA[Enrique Juan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Montoya Pedrón]]></surname>
<given-names><![CDATA[Arquímedes]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Oriente  ]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Hospital General Docente Juan Bruno Zayas Alfonso  ]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>40</volume>
<numero>3</numero>
<fpage>16</fpage>
<lpage>27</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282019000300016&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282019000300016&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282019000300016&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Durante un procedimiento quirúrgico es esencial inducir al paciente estados de inconsciencia, amnesia, analgesia y relajación muscular, sin embargo, debido a la inexactitud en la monitorización de la anestesia se reportan casos de despertar intraoperatorio. A causa de la incidencia de este fenómeno, el Centro de Estudios de Neurociencias, Procesamiento de Imágenes y Señales en la Universidad de Oriente, Cuba, lleva a cabo la implementación de un prototipo de monitor de anestesia basado en el reconocimiento automático de estados de sedación en las señales electroencefalográficas usando técnicas de Inteligencia Artificial. Para alcanzar el objetivo propuesto se evaluó el desempeño de un clasificador Naive Bayes y tres Máquinas de Aprendizaje: Redes Neuronales Artificiales con cinco topologías diferentes, Sistemas de Inferencia Difusa basada en Redes Adaptativas y las Máquinas de Soporte Vectorial para reconocer tres estados de sedación caracterizados por nueve parámetros de potencia obtenidos a partir del espectro de frecuencia de las señales registradas por los canales electroencefalográficos frontales F4 y Fz. Como resultados de los experimentos se reconocieron los estados de Sedación Profunda, Sedación Moderada y Sedación Ligera con una Exactitud de 96.12%, 90.06% y 90.24% respectivamente usando las Máquinas de Soporte Vectorial y los registros del canal electroencefalográfico F4.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT During a surgical procedure it is essential induce to the patient, unconsciousness states, amnesia, analgesia and muscle relaxation, however, cases of intraoperative awareness are reported for the inaccuracy in monitoring anesthesia. Due the incidence of this phenomenon, the Center for Neuroscience Studies, Images and Signals Processing from Universidad de Oriente, Cuba, is carried out the development of an anesthesia monitor prototype, based on automatic recognition of sedation states in electroencephalographic signals using Artificial Intelligence techniques. To achieve the proposed objective, were evaluated the performance of a Naive Bayes classifier and three Machines Learning: Artificial Neural Networks with five different topologies, Adaptive Network Based Fuzzy Inference System and Support Vector Machines to recognize three sedation states characterized by nine power parameters obtained from the frequency spectrum of the signals recorded by two electroencephalographic channels front F4 and Fz. As results of the experiments, the states Profound Sedation, Moderate Sedation and Mild Sedation were recognized with an Accuracy of 96.12%, 90.06% and 90.24% respectively using Support Vector Machines and the registers of F4 electroencephalographic channel.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Máquinas de Aprendizaje]]></kwd>
<kwd lng="es"><![CDATA[Estados de Sedación]]></kwd>
<kwd lng="es"><![CDATA[Señales Electroencefalográficas]]></kwd>
<kwd lng="en"><![CDATA[Machines Learning]]></kwd>
<kwd lng="en"><![CDATA[Sedation States]]></kwd>
<kwd lng="en"><![CDATA[Electroencephalographic Signals]]></kwd>
</kwd-group>
</article-meta>
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