<?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>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322023000400038</article-id>
<article-id pub-id-type="doi">10.17488/rmib.44.4.3</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Feature Selection of Motor Activity in Intervals of Time with Genetics Algorithms for Depression Detection]]></article-title>
<article-title xml:lang="es"><![CDATA[Selección de Características de la Actividad Motora en Intervalos de Tiempo con Algoritmos Genéticos para la Detección de Depresión]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Espino-Salinas]]></surname>
<given-names><![CDATA[Carlos H.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[Carlos E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Reyna]]></surname>
<given-names><![CDATA[Ana G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Luna-García]]></surname>
<given-names><![CDATA[Huizilopoztli]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gamboa-Rosales]]></surname>
<given-names><![CDATA[Hamurabi]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morgan-Benita]]></surname>
<given-names><![CDATA[Jorge A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Celaya-Padilla]]></surname>
<given-names><![CDATA[José M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[Jorge I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autonoma de Zacatecas  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>44</volume>
<numero>spe1</numero>
<fpage>38</fpage>
<lpage>52</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322023000400038&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322023000400038&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322023000400038&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract It is estimated that depression affects more than 300 million people in worldwide. Unfortunately, the current method of psychiatric evaluation requires a great effort on the part of clinicians to collect complete information. The aim of this paper is determine the optimal time intervals to detect depression using genetic algorithms and machine learning techniques; from motor activity readings of 55 participants during a week at one-minute intervals. The time intervals with the best performance in detecting depression in individuals were selected by applying Genetic Algorithms (GA). Methodology. 385 observations of the study participants were evaluated, obtaining an accuracy of 83.0 % with Logistic Regression (LR). Conclusion. There is a relationship between motor activity and people with depression since it is possible to detect it using machine learning techniques. However, the changes in the variables of the time intervals could be established as key factors since, at different times, they could give good or bad results because the motor activity in the patients could vary. However, the results present a first approximation for developing tools that help the opportune and objective diagnosis of depression.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Se estima que la depresión afecta a más de 300 millones de personas en el mundo. Desafortunadamente, el método de evaluación psiquiátrica actual requiere un gran esfuerzo por parte de los médicos para recopilar información completa. Objetivo. Determinar los intervalos de tiempo óptimos para detectar depresión mediante algoritmos genéticos y técnicas de aprendizaje automático, a partir de las lecturas de actividad motora de 55 sujetos durante una semana en intervalos de un minuto. Los intervalos de tiempo con mejor desempeño en la detección de depresión en individuos fueron seleccionados aplicando algoritmos genéticos. Metodología. Se evaluaron 385 observaciones de los sujetos de estudio, obteniendo una precisión del 83.0 % con Regresión Logística (LR). Conclusión. Existe una relación entre la actividad motora y las personas con depresión ya que es posible detectarla utilizando técnicas de aprendizaje automático. Sin embargo, los cambios en las variables de los intervalos de tiempo podrían establecerse como factores clave ya que en diferentes momentos podrían dar buenos o malos resultados debido a que la actividad motora en los pacientes podría llegar a variar. No obstante, los resultados presentan una primera aproximación para el desarrollo de herramientas que ayuden al diagnóstico oportuno y objetivo de la depresión.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[depression]]></kwd>
<kwd lng="en"><![CDATA[feature selection]]></kwd>
<kwd lng="en"><![CDATA[genetic algorithm]]></kwd>
<kwd lng="en"><![CDATA[motor activity]]></kwd>
<kwd lng="es"><![CDATA[actividad motora]]></kwd>
<kwd lng="es"><![CDATA[algoritmos genéticos]]></kwd>
<kwd lng="es"><![CDATA[depresión]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[selección de características]]></kwd>
</kwd-group>
</article-meta>
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