<?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>1028-9933</journal-id>
<journal-title><![CDATA[Revista Información Científica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. inf. cient.]]></abbrev-journal-title>
<issn>1028-9933</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas Guantánamo]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1028-99332022000300007</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estrategia para la toma de decisiones en el reconocimiento automático de estados de sedación anestésica]]></article-title>
<article-title xml:lang="en"><![CDATA[Decision-making strategy for automatic recognition of sedation states]]></article-title>
<article-title xml:lang="pt"><![CDATA[Estratégia de tomada de decisão para reconhecimento automático de estados de sedação]]></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[Rodríguez-Aldana]]></surname>
<given-names><![CDATA[Yissel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marañon-Reyes]]></surname>
<given-names><![CDATA[Enrique J.]]></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 Facultad de Ingeniería en Telecomunicaciones, Informática y Biomédica ]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Oriente Centro de Estudios de Neurociencias, Procesamiento de Imágenes y Señales ]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Hospital General Docente &#8220;Dr. 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>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>101</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1028-99332022000300007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1028-99332022000300007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1028-99332022000300007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  La Anestesiología es la especialidad médica dedicada a la atención específica de los pacientes durante procedimientos quirúrgicos y en cuidados intensivos. Esta especialidad basada en los avances científicos y tecnológicos, ha incorporado el uso del monitoreo electroencefalográfico, facilitando el control continuo de estados de sedación anestésica durante las cirugías, con una adecuada concentración de fármacos.  Objetivo: Proponer una estrategia de clasificación para el reconocimiento automático de tres estados de sedación anestésica en señales electroencefalográficas.  Método: Se utilizaron con consentimiento informado escrito los registros electroencefalográficos de 27 pacientes sometidos a cirugía abdominal, excluyendo aquellos con antecedentes de epilepsia, enfermedades cerebrovasculares y otras afecciones neurológicas. Se aplicaron en total 12 fármacos anestésicos y dos relajantes musculares con montaje de 19 electrodos según el Sistema Internacional 10-20. Se eliminaron artefactos en los registros y se aplicaron técnicas de Inteligencia artificial para realizar el reconocimiento automático de los estados de sedación.  Resultados:  Se propuso una estrategia basada en el uso de máquinas de soporte vectorial con algoritmo multiclase Uno-Contra-Resto y la métrica Similitud Coseno, para realizar el reconocimiento automático de tres estados de sedación: profundo, moderado y ligero, en señales registradas por el canal frontal F4 y los occipitales O1 y O2. Se realizó una comparación de la propuesta con otros métodos de clasificación.  Conclusiones:  Se computa una exactitud balanceada del 92,67 % en el reconocimiento de los tres estados de sedación en las señales registradas por el canal electroencefalográfico F4, lo cual favorece el desarrollo de la monitorización anestésica.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction: Anesthesiology is the medical specialty concerned with the specific care of patients during surgical and intensive care procedures. This specialty, based on scientific and technological advances, has incorporated the use of electroencephalographic monitoring, facilitating the continuous control in the use of anesthesia for patient´s sedation states during surgeries, with an adequate concentration of drugs.  Objective:  Proposal for a classification strategy for automatic recognition of three sedation states in electroencephalographic signals.  Methods:  We used, with written informed consent, the electroencephalographic records of 27 patients undergoing abdominal surgery, excluding those with a history of epilepsy, cerebrovascular disease and other neurological conditions. A total of 12 drugs to produce anesthesia and two muscle relaxants with 19 electrodes, mounted according to the International System 10 -20, were applied. Artifacts in the records were eliminated and artificial intelligence techniques were applied to perform automatic recognition of sedation states.  Results:  A strategy based on the use of support vector machines with a multiclass algorithm One-against-Rest and the Cosine Similarity metric was proposed to perform the automatic recognition of three sedation states: deep, moderate and light, in signals recorded by the frontal channel F4 and the occipital channels O1 and O2. A comparison was carried out between the proposal showed and other classification methods.  Conclusions:  A balanced accuracy of 92.67% is computed about the recognition of the three states of sedation in the signals recorded by the electroencephalographic channel F4, which helps in a better anesthetic monitoring process.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[RESUMO  Introdução:  A Anestesiologia é a especialidade médica dedicada ao atendimento específico de pacientes durante procedimentos cirúrgicos e em terapia intensiva. Essa especialidade, baseada nos avanços científicos e tecnológicos, incorporou o uso da monitorização eletroencefalográfica, facilitando o controle contínuo dos estados de sedação anestésica durante as cirurgias, com concentração adequada de fármacos.  Objetivo:  Propor uma estratégia de classificação para o reconhecimento automático de três estados de sedação anestésica em sinais eletroencefalográficos.  Método:  Foram utilizados registros eletroencefalográficos de 27 pacientes submetidos à cirurgia abdominal com consentimento informado por escrito, excluindo aqueles com histórico de epilepsia, doenças cerebrovasculares e outras condições neurológicas. Um total de 12 drogas anestésicas e dois relaxantes musculares foram aplicados com um conjunto de 19 eletrodos de acordo com o Sistema Internacional 10-20. Artefatos nos prontuários foram removidos e técnicas de inteligência artificial foram aplicadas para realizar o reconhecimento automático dos estados de sedação.  Resultados:  Foi proposta uma estratégia baseada no uso de máquinas de vetores de suporte com algoritmo One-Against-Rest multiclasse e a métrica Cosine Similarity para realizar o reconhecimento automático de três estados de sedação: profundo, moderado e leve, em sinais registrados pelo canal frontal F4 e os canais occipitais O1 e O2. Foi feita uma comparação da proposta com outros métodos de classificação.  Conclusões:  Uma acurácia equilibrada de 92,67% é computada no reconhecimento dos três estados de sedação nos sinais registrados pelo canal eletroencefalográfico F4, o que favorece o desenvolvimento da monitorização anestésica.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[señales electroencefalográficas]]></kwd>
<kwd lng="es"><![CDATA[estados de sedación anestésica]]></kwd>
<kwd lng="es"><![CDATA[reconocimiento automático]]></kwd>
<kwd lng="es"><![CDATA[máquinas de soporte vectorial]]></kwd>
<kwd lng="en"><![CDATA[electroencephalographic signals]]></kwd>
<kwd lng="en"><![CDATA[anesthetic sedation states]]></kwd>
<kwd lng="en"><![CDATA[automatic recognition]]></kwd>
<kwd lng="en"><![CDATA[support vector machines]]></kwd>
<kwd lng="pt"><![CDATA[sinais eletroencefalográficos]]></kwd>
<kwd lng="pt"><![CDATA[estados de sedação anestésica]]></kwd>
<kwd lng="pt"><![CDATA[reconhecimento automático]]></kwd>
<kwd lng="pt"><![CDATA[máquinas de vetor de suporte]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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