<?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>0121-750X</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[ing.]]></abbrev-journal-title>
<issn>0121-750X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-750X2025000100003</article-id>
<article-id pub-id-type="doi">10.14483/23448393.22185</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection]]></article-title>
<article-title xml:lang="es"><![CDATA[Selección optima de funciones de modo intrínseco aplicada a la detección de convulsiones]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guerrero-Otoya]]></surname>
<given-names><![CDATA[Luis Daladier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bueno-López]]></surname>
<given-names><![CDATA[Maximiliano]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Giraldo]]></surname>
<given-names><![CDATA[Eduardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Molinas]]></surname>
<given-names><![CDATA[Marta]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Cauca  ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Tecnológica de Pereira  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,NTNU Department of Engineering Cybernetics ]]></institution>
<addr-line><![CDATA[Trondheim ]]></addr-line>
<country>Norway</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<volume>30</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-750X2025000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-750X2025000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-750X2025000100003&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Context: Epilepsy is a severe chronic neurological disorder with considerable incidence due to recurrent seizures. These seizures can be detected and diagnosed noninvasively using an electroencephalogram. Empirical mode decomposition has shown excellent results in identifying epileptic crises.  Method: This study addressed a significant gap by proposing a novel approach for the automated selection of the most relevant intrinsic mode functions (IMFs) using empirical mode decomposition and discrimination metrics such as the Minkowski distance, the mean square error, cross-correlation, and the entropy function. The main objective was to address the challenge of determining the optimal number of IMFs required to accurately reconstruct brain activity signals.  Results: The results were promising, as they facilitated the identification of IMFs that contained the most relevant information, marking a significant advancement in the field. To validate these findings, standard methods including the correlation coefficient, the p-value, and the Wasserstein distance were employed. Additionally, an EEGLAB-based brain mapping was conducted, adding robustness and credibility to the results obtained.  Conclusions: Our method is a fundamental tool that enhances epileptic seizure identification from EEG signals, with significant clinical implications in the diagnosis and treatment of epilepsy.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto: La epilepsia es un trastorno neurológico crónico grave con una incidencia considerable debido a convulsiones recurrentes. Estas convulsiones pueden ser detectadas de manera no invasiva y diagnosticadas mediante un electroencefalograma. La descomposición modal empírica ha mostrado excelentes resultados en la identificación de crisis epilépticas.  Métodos: Este estudio abordó una brecha significativa al proponer un enfoque novedoso para la selección automatizada de las funciones de modo intrínseco (IMF) más relevantes utilizando descomposición empírica de modo y métricas de discriminación tales como la distancia de Minkowski, el error cuadrático medio, la correlación cruzada y la función de entropía. El objetivo primario fue abordar el desafío de determinar el número óptimo de IMF requeridas para reconstruir con precisión las señales de actividad cerebral.  Resultados: Los resultados fueron prometedores, pues facilitaron la identificación de IMF que contenían la información más relevante, marcando un avance significativo en el campo. Para validar estos hallazgos, se emplearon métodos estándar, incluyendo el coeficiente de correlación, el valor p y la métrica de Wasserstein. Además, se realizó un mapeo cerebral con EEGLAB, lo que agregó robustez y credibilidad a los resultados obtenidos.  Conclusiones: Nuestro método es una herramienta fundamental que permite mejorar la identificación de convulsiones epilépticas a partir de señales de EEG, con importantes implicaciones clínicas en el diagnóstico y tratamiento de la epilepsia.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[seizure identification]]></kwd>
<kwd lng="en"><![CDATA[empirical mode decomposition]]></kwd>
<kwd lng="en"><![CDATA[optimal selection of IMFs]]></kwd>
<kwd lng="en"><![CDATA[intrinsic mode functions]]></kwd>
<kwd lng="en"><![CDATA[discrimination metrics]]></kwd>
<kwd lng="es"><![CDATA[Identificación de convulsiones]]></kwd>
<kwd lng="es"><![CDATA[descomposición modal empírica]]></kwd>
<kwd lng="es"><![CDATA[selección óptima de IMFs]]></kwd>
<kwd lng="es"><![CDATA[funciones modales intrínsecas]]></kwd>
<kwd lng="es"><![CDATA[métricas de discriminación]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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