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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "https://jats.nlm.nih.gov/publishing/1.3/JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xml:lang="en">
  <front xmlns:xlink="http://www.w3.org/1999/xlink">
    <journal-meta>
      <journal-id journal-id-type="elibrary">9004</journal-id>
      <journal-title-group>
        <journal-title>Problems of information security. Computer systems</journal-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Проблемы информационной безопасности. Компьютерные системы</trans-title>
        </trans-title-group>
      </journal-title-group>
      <issn pub-type="epub">2071-8217</issn>
    </journal-meta>
    <article-meta xmlns:xlink="http://www.w3.org/1999/xlink">
      <article-id pub-id-type="publisher-id">8</article-id>
      <article-id pub-id-type="doi">10.48612/jisp/b4dd-gma4-epzv</article-id>
      <title-group>
        <article-title>Digitalization and identification of ECG signals using wavelet technologies</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Цифровизация и идентификация ЭКГ-сигналов с применением вейвлет-технологий</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-6289-3295</contrib-id>
          <contrib-id contrib-id-type="scopus">57200960264</contrib-id>
          <name>
            <surname>Sikarev</surname>
            <given-names>Igor</given-names>
          </name>
          <xref ref-type="aff" rid="aff1"/>
          <email>sikarev@yandex.ru</email>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Sakharov</surname>
            <given-names>Vladimir</given-names>
          </name>
          <xref ref-type="aff" rid="aff2"/>
          <email>saharovvv@gumrf.ru</email>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Ivanyuk</surname>
            <given-names>Vladimir</given-names>
          </name>
          <xref ref-type="aff" rid="aff3"/>
        </contrib>
      </contrib-group>
      <aff id="aff1">Russian State Hydrometeorological University</aff>
      <aff id="aff2">Admiral S.O. Makarov State University of Marine and River Fleet</aff>
      <aff id="aff3">Admiral Makarov State University of Maritime and Inland Shipping</aff>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2022-06-10">
        <day>10</day>
        <month>06</month>
        <year>2022</year>
      </pub-date>
      <issue>2</issue>
      <fpage>82</fpage>
      <lpage>97</lpage>
      <self-uri xmlns:xlink="http://www.w3.org/1999/xlink" content-type="pdf" xlink:href="https://jisp.spbstu.ru/userfiles/files/2022_2_angl.pdf"/>
      <abstract xml:lang="en">
        <p>A method for identifying signals based on the results of electrocardiogram (ECG) processing
performed based on wavelet technologies is considered. The use of digital technologies for processing
and diagnostics of ECG signals using wavelet analysis can significantly improve the efficiency
and quality of evaluation of pacemaker settings during implantation, as well as in the process of
correction of functional modes, diagnostics, in order to eliminate postoperative complications, etc.
Digital processing of complex cardiac signals at a qualitatively new level is an indispensable condition
for radically improving the processing of the current values of the diagnosed parameters, the
widespread use of digital tools for making informed and effective decisions in the field of medical
care, as well as for information support of identification processes. A method of approximation is
considered and an algorithm for analyzing ECG diagrams obtained during implantation and in the
process of choosing the modes of functioning of pacemakers based on the wavelet, transform is
given. The presence of high–frequency components and short-term pulses in the spectrum of ECG
signals, the evaluation of which is practically impossible by the traditionally used methods of spectral
analysis, determined the choice of a method for digitalizing the decomposition of signals into
basic frequency rhythms for parametric evaluation of QRS complexes. The approximation method
is based on the use of wavelet analysis, which allows deep investigation of such modes. Examples
of the use of wavelet analysis for the approximation of ECG diagrams using cubic splines whose
interpolation nodes are located on an uneven grid are given. Digital technologies are implemented
using the tools of the MATLAB computing environment</p>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>electrocardiogram</kwd>
        <kwd>parametric estimation</kwd>
        <kwd>identification</kwd>
        <kwd>wavelet technologies</kwd>
        <kwd>Dobshy wavelets</kwd>
        <kwd>cubic spline</kwd>
        <kwd>signal reconstruction levels</kwd>
        <kwd>wavelet decomposition coefficients</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
