<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2226</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>*</pageFirst>
    <pageLast>*</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11/5</issue>
    <volume>2023</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-09-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning</title>
    <abstract language="eng">Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.</abstract>
    <parentTitle language="eng">Journal of Complex Networks</parentTitle>
    <identifier type="url">https://academic.oup.com/comnet/article-abstract/11/5/cnad030/7260365?redirectedFrom=fulltext</identifier>
    <identifier type="doi">https://doi.org/10.1093/comnet/cnad030</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="HAB_Review">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Aruane M. Pineda</author>
    <author>Caroline L. Alves</author>
    <author>Michael Möckel</author>
    <author>Thaise Graziele L de O Toutain</author>
    <author>Joel Augusto Moura Porto</author>
    <author>Francisco A. Rodrigues</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektrokardiogramm</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Älterer Mensch</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Heranwachsender</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="institutes" number="">Medizinische IT &amp; Simulation</collection>
  </doc>
</export-example>
