<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-01T05:32:01Z</responseDate><request verb="GetRecord" identifier="oai:riuma.uma.es:10630/28866" metadataPrefix="mods">https://riuma.uma.es/rest/oai/request</request><GetRecord><record><header><identifier>oai:riuma.uma.es:10630/28866</identifier><datestamp>2026-02-03T10:55:41Z</datestamp><setSpec>com_10630_2254</setSpec><setSpec>col_10630_37953</setSpec></header><metadata><mods:mods xmlns:doc="http://www.lyncode.com/xoai" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
   <mods:name>
      <mods:namePart>Arco, Juan E.</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Gallego-Molina, Nicolás J.</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Ortiz-García, Andrés</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Arroyo-Alvis, Katy</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>López-Pérez, P. Javier</mods:namePart>
   </mods:name>
   <mods:extension>
      <mods:dateAvailable encoding="iso8601">2024-01-18T10:50:53Z</mods:dateAvailable>
   </mods:extension>
   <mods:extension>
      <mods:dateAccessioned encoding="iso8601">2024-01-18T10:50:53Z</mods:dateAccessioned>
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   <mods:originInfo>
      <mods:dateIssued encoding="iso8601">2023-12-15</mods:dateIssued>
   </mods:originInfo>
   <mods:identifier type="citation">Juan E. Arco, Nicolás J. Gallego-Molina, Andrés Ortiz, Katy Arroyo-Alvis, P. Javier López-Pérez, Identifying HRV patterns in ECG signals as early markers of dementia, Expert Systems with Applications, Volume 243, 2024, 122934, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.122934. (https://www.sciencedirect.com/science/article/pii/S095741742303436X)</mods:identifier>
   <mods:identifier type="uri">https://hdl.handle.net/10630/28866</mods:identifier>
   <mods:identifier type="doi">10.1016/j.eswa.2023.122934</mods:identifier>
   <mods:abstract>The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These&#xd;
tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening&#xd;
analysis and disentangle the information that they contain. We propose in this work a novel method for&#xd;
evaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers a&#xd;
complete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to the&#xd;
final classification stage. Numerous metrics are computed from the original data in terms of different domains&#xd;
(time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). The&#xd;
resulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines,&#xd;
Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an area&#xd;
under the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI)&#xd;
patients, showing that ECG contain crucial information for predicting the appearance of this pathology. These&#xd;
results are specially relevant given the fact that ECG acquisition is much more affordable and less invasive&#xd;
than brain imaging used in most of these intelligent systems, allowing our method to be used in environments&#xd;
of any socioeconomic range.</mods:abstract>
   <mods:language>
      <mods:languageTerm>eng</mods:languageTerm>
   </mods:language>
   <mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by/4.0/</mods:accessCondition>
   <mods:accessCondition type="useAndReproduction">open access</mods:accessCondition>
   <mods:accessCondition type="useAndReproduction">Atribución 4.0 Internacional</mods:accessCondition>
   <mods:subject>
      <mods:topic>Electrocardiografía</mods:topic>
   </mods:subject>
   <mods:subject>
      <mods:topic>Demencia</mods:topic>
   </mods:subject>
   <mods:subject>
      <mods:topic>Procesado de señales</mods:topic>
   </mods:subject>
   <mods:titleInfo>
      <mods:title>Identifying HRV patterns in ECG signals as early markers of dementia</mods:title>
   </mods:titleInfo>
   <mods:genre>journal article</mods:genre>
</mods:mods>
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