<?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>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992019000100001</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Using Partial Least Squares for Nonconvulsive Epileptic Seizure Detection]]></article-title>
<article-title xml:lang="es"><![CDATA[Detección de crisis epilépticas no convulsivas usando Mínimos Cuadrados Parciales]]></article-title>
</title-group>
<contrib-group>
<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[Sanabria Macias]]></surname>
<given-names><![CDATA[Frank]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marañón Reyes]]></surname>
<given-names><![CDATA[Enrique J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez Rodríguez]]></surname>
<given-names><![CDATA[Valia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Van Huffel]]></surname>
<given-names><![CDATA[Sabine]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hunyadi]]></surname>
<given-names><![CDATA[Barbála]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Oriente Center of Neuroscience and Signals and Image Processing ]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Aston University  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<aff id="Af3">
<institution><![CDATA[,Stadius Center for Dynamical Systems Signal Processing and Data Analytics and IMEC ]]></institution>
<addr-line><![CDATA[ Leuven]]></addr-line>
<country>Belgium</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<volume>13</volume>
<numero>1</numero>
<fpage>1</fpage>
<lpage>13</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992019000100001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992019000100001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992019000100001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Nonconvulsive status epilepticus (NCSE) is a condition where the patient is exposed to abnormally prolonged nonconvulsive epileptic seizures (NCES)(epileptic seizures without evident physical symptoms). Hence, the diagnosis can only be stated by means of EEG monitoring. NCSE and NCES are associated with severe irreversible brain damage and poor outcome. Hence, the prompt recognition of patients at risk of su&#64256;ering NCSE is necessary in order to manage them properly and to prevent further brain injury. However, despite the clinical e&#64256;orts to manage NCES and NCSE, and improve the patient&#8217;s outcome, monitoring this pathology in real-time is very di&#64259;cult. In a previous work of these authors, a patient speci&#64257;c method is proposed to detect the NCES. This method identi&#64257;es the NCES by exploiting the similarity between the &#64257;rst NCES detected by the physician on the EEG and the rest of NCES in the recording. The method used a support vector machine classi&#64257;er to perform the classi&#64257;cation, obtaining speci&#64257;city, and sensitivity, results over 98%. However, the method was vulnerable to missclassify epochs with EEG patterns resembling a NCES. In this paper, we propose a complementary method based in partial least squares (PLS) to improve the identi&#64257;cation of the NCES patterns of the previously proposed method in dubious EEG segments. The proposed method improved the SVM based model performance obtaining speci&#64257;city ans sensitivity values over 99%.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El status epiléptico no convulsivo (NCSE, por sus siglas en inglés) es una afección en la que el paciente está expuesto a crisis epilépticas no convulsivas (NCES, por sus siglas en inglés) anormalmente prolongadas (crisis epilépticas sin síntomas físicos evidentes). Por tanto, el diagnóstico solo puede establecerse mediante el monitoreo del EEG. NCSE y NCES están asociados con daño cerebral irreversible y altos índices de morbilidad y mortalidad. Por ello es necesario el reconocimiento rápido de los pacientes en riesgo de sufrir NCSE a fin de manejarlos adecuadamente y prevenir más lesiones cerebrales. Sin embargo, a pesar de los esfuerzos clínicos para manejar NCES y NCSE y mejorar el resultado del paciente, monitorear esta patología en tiempo real es muy difícil. En un trabajo previo de estos autores, se propone un método paciente-específico para detectar el NCES. Este método identifica el NCES al explotar la similitud entre la primera NCES detectada por el médico en el EEG y el resto de NCES en la grabación. El método utiliza una máquina de vectores de soporte para realizar la clasificación, obteniendo resultados de especificidad y sensibilidad, superiores al 98 %. Sin embargo, el método demuestra ser vulnerable a clasificar mal las épocas con patrones de EEG que se asemejan a NCES. En este artículo, proponemos un método complementario basado en mínimos cuadrados parciales (PLS) para mejorar la identificación de los patrones NCES del método propuesto en (Rodríguez Aldana et al., 2018) para segmentos de EEG dudosos. El método aquí propuesto mejoró el rendimiento del modelo basado en SVM obteniendo valores de especificidad y sensibilidad superiores al 99 %.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[EEG]]></kwd>
<kwd lng="en"><![CDATA[multiway data analysis]]></kwd>
<kwd lng="en"><![CDATA[nonconvulsive epileptic seizures]]></kwd>
<kwd lng="en"><![CDATA[partial least squares]]></kwd>
<kwd lng="es"><![CDATA[crisis epilépticas no convulsivas]]></kwd>
<kwd lng="es"><![CDATA[EEG, mínimos cuadrados parciales]]></kwd>
<kwd lng="es"><![CDATA[análisis multimodal]]></kwd>
</kwd-group>
</article-meta>
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