PVC discrimination using the QRS power spectrum and self-organizing maps.
Identifieur interne : 000736 ( Main/Exploration ); précédent : 000735; suivant : 000737PVC discrimination using the QRS power spectrum and self-organizing maps.
Auteurs : M L Talbi [Algérie] ; A. CharefSource :
- Computer methods and programs in biomedicine [ 1872-7565 ] ; 2009.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Automatisation (méthodes), Biologie informatique (méthodes), Cardiologie (instrumentation), Cardiologie (méthodes), Extrasystoles ventriculaires (diagnostic), Extrasystoles ventriculaires (physiopathologie), Humains (MeSH), Intelligence artificielle (MeSH), Modèles statistiques (MeSH), Reconnaissance automatique des formes (méthodes), Reproductibilité des résultats (MeSH), Sensibilité et spécificité (MeSH), Traitement du signal assisté par ordinateur (MeSH), Troubles du rythme cardiaque (diagnostic), Troubles du rythme cardiaque (physiopathologie), Électrocardiographie (méthodes).
- MESH :
- diagnostic : Extrasystoles ventriculaires, Troubles du rythme cardiaque.
- méthodes : Automatisation, Biologie informatique, Cardiologie, Reconnaissance automatique des formes, Électrocardiographie.
- physiopathologie : Extrasystoles ventriculaires, Troubles du rythme cardiaque.
- instrumentation : Algorithmes, Cardiologie, Humains, Intelligence artificielle, Modèles statistiques, Reproductibilité des résultats, Sensibilité et spécificité, Traitement du signal assisté par ordinateur.
English descriptors
- KwdEn :
- Algorithms (MeSH), Arrhythmias, Cardiac (diagnosis), Arrhythmias, Cardiac (physiopathology), Artificial Intelligence (MeSH), Automation (methods), Cardiology (instrumentation), Cardiology (methods), Computational Biology (methods), Electrocardiography (methods), Humans (MeSH), Models, Statistical (MeSH), Pattern Recognition, Automated (methods), Reproducibility of Results (MeSH), Sensitivity and Specificity (MeSH), Signal Processing, Computer-Assisted (MeSH), Ventricular Premature Complexes (diagnosis), Ventricular Premature Complexes (physiopathology).
- MESH :
- diagnosis : Arrhythmias, Cardiac, Ventricular Premature Complexes.
- instrumentation : Cardiology.
- methods : Automation, Cardiology, Computational Biology, Electrocardiography, Pattern Recognition, Automated.
- physiopathology : Arrhythmias, Cardiac, Ventricular Premature Complexes.
- Algorithms, Artificial Intelligence, Humans, Models, Statistical, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted.
Abstract
This paper deals with the discrimination of premature ventricular contraction (PVC) arrhythmia using the fractal behavior of the power spectrum density of the QRS complexes. The linear interpolation of the QRS complex power spectrum density in Bode diagram in two different frequency intervals gives two straight lines with two different slopes. The scatter plot of one slope versus the other shows that there exists two distinct regions which represent the normal beats and the PVC beats. Therefore the PVC beats are classified using a self-organizing map fed by the two slopes of the QRS complex power spectrum. The MIT/BIH arrhythmia database is then used to evaluate the usefulness of the proposed method in the discrimination of the premature ventricular contraction (PVC) arrhythmia. The results have indicated that the method has achieved 82.71% of sensitivity and 88.06% of specificity over 46 records from the MIT-BIH arrhythmia database.
DOI: 10.1016/j.cmpb.2008.12.009
PubMed: 19215994
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Charef</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2009">2009</date><idno type="RBID">pubmed:19215994</idno><idno type="pmid">19215994</idno><idno type="doi">10.1016/j.cmpb.2008.12.009</idno><idno type="wicri:Area/Main/Corpus">000749</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000749</idno><idno type="wicri:Area/Main/Curation">000749</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000749</idno><idno type="wicri:Area/Main/Exploration">000749</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">PVC discrimination using the QRS power spectrum and self-organizing maps.</title><author><name sortKey="Talbi, M L" sort="Talbi, M L" uniqKey="Talbi M" first="M L" last="Talbi">M L Talbi</name><affiliation wicri:level="1"><nlm:affiliation>Laboratoire de traitement du signal, Département d'électronique, Université Mentouri de Constantine, Constantine 25000, Algeria. mltalbi@yahoo.fr</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>Laboratoire de traitement du signal, Département d'électronique, Université Mentouri de Constantine, Constantine 25000</wicri:regionArea><wicri:noRegion>Constantine 25000</wicri:noRegion></affiliation></author><author><name sortKey="Charef, A" sort="Charef, A" uniqKey="Charef A" first="A" last="Charef">A. Charef</name></author></analytic><series><title level="j">Computer methods and programs in biomedicine</title><idno type="eISSN">1872-7565</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Arrhythmias, Cardiac (diagnosis)</term><term>Arrhythmias, Cardiac (physiopathology)</term><term>Artificial Intelligence (MeSH)</term><term>Automation (methods)</term><term>Cardiology (instrumentation)</term><term>Cardiology (methods)</term><term>Computational Biology (methods)</term><term>Electrocardiography (methods)</term><term>Humans (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Pattern Recognition, Automated (methods)</term><term>Reproducibility of Results (MeSH)</term><term>Sensitivity and Specificity (MeSH)</term><term>Signal Processing, Computer-Assisted (MeSH)</term><term>Ventricular Premature Complexes (diagnosis)</term><term>Ventricular Premature Complexes (physiopathology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Automatisation (méthodes)</term><term>Biologie informatique (méthodes)</term><term>Cardiologie (instrumentation)</term><term>Cardiologie (méthodes)</term><term>Extrasystoles ventriculaires (diagnostic)</term><term>Extrasystoles ventriculaires (physiopathologie)</term><term>Humains (MeSH)</term><term>Intelligence artificielle (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Reconnaissance automatique des formes (méthodes)</term><term>Reproductibilité des résultats (MeSH)</term><term>Sensibilité et spécificité (MeSH)</term><term>Traitement du signal assisté par ordinateur (MeSH)</term><term>Troubles du rythme cardiaque (diagnostic)</term><term>Troubles du rythme cardiaque (physiopathologie)</term><term>Électrocardiographie (méthodes)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Arrhythmias, Cardiac</term><term>Ventricular Premature Complexes</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Extrasystoles ventriculaires</term><term>Troubles du rythme cardiaque</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Cardiology</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Automation</term><term>Cardiology</term><term>Computational Biology</term><term>Electrocardiography</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Automatisation</term><term>Biologie informatique</term><term>Cardiologie</term><term>Reconnaissance automatique des formes</term><term>Électrocardiographie</term></keywords><keywords scheme="MESH" qualifier="physiopathologie" xml:lang="fr"><term>Extrasystoles ventriculaires</term><term>Troubles du rythme cardiaque</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Arrhythmias, Cardiac</term><term>Ventricular Premature Complexes</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Humans</term><term>Models, Statistical</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term><term>Signal Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="fr"><term>Algorithmes</term><term>Cardiologie</term><term>Humains</term><term>Intelligence artificielle</term><term>Modèles statistiques</term><term>Reproductibilité des résultats</term><term>Sensibilité et spécificité</term><term>Traitement du signal assisté par ordinateur</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper deals with the discrimination of premature ventricular contraction (PVC) arrhythmia using the fractal behavior of the power spectrum density of the QRS complexes. The linear interpolation of the QRS complex power spectrum density in Bode diagram in two different frequency intervals gives two straight lines with two different slopes. The scatter plot of one slope versus the other shows that there exists two distinct regions which represent the normal beats and the PVC beats. Therefore the PVC beats are classified using a self-organizing map fed by the two slopes of the QRS complex power spectrum. The MIT/BIH arrhythmia database is then used to evaluate the usefulness of the proposed method in the discrimination of the premature ventricular contraction (PVC) arrhythmia. The results have indicated that the method has achieved 82.71% of sensitivity and 88.06% of specificity over 46 records from the MIT-BIH arrhythmia database.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19215994</PMID><DateCompleted><Year>2009</Year><Month>07</Month><Day>15</Day></DateCompleted><DateRevised><Year>2009</Year><Month>04</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1872-7565</ISSN><JournalIssue CitedMedium="Internet"><Volume>94</Volume><Issue>3</Issue><PubDate><Year>2009</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Computer methods and programs in biomedicine</Title><ISOAbbreviation>Comput Methods Programs Biomed</ISOAbbreviation></Journal><ArticleTitle>PVC discrimination using the QRS power spectrum and self-organizing maps.</ArticleTitle><Pagination><MedlinePgn>223-31</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.cmpb.2008.12.009</ELocationID><Abstract><AbstractText>This paper deals with the discrimination of premature ventricular contraction (PVC) arrhythmia using the fractal behavior of the power spectrum density of the QRS complexes. The linear interpolation of the QRS complex power spectrum density in Bode diagram in two different frequency intervals gives two straight lines with two different slopes. The scatter plot of one slope versus the other shows that there exists two distinct regions which represent the normal beats and the PVC beats. Therefore the PVC beats are classified using a self-organizing map fed by the two slopes of the QRS complex power spectrum. The MIT/BIH arrhythmia database is then used to evaluate the usefulness of the proposed method in the discrimination of the premature ventricular contraction (PVC) arrhythmia. The results have indicated that the method has achieved 82.71% of sensitivity and 88.06% of specificity over 46 records from the MIT-BIH arrhythmia database.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Talbi</LastName><ForeName>M L</ForeName><Initials>ML</Initials><AffiliationInfo><Affiliation>Laboratoire de traitement du signal, Département d'électronique, Université Mentouri de Constantine, Constantine 25000, Algeria. mltalbi@yahoo.fr</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Charef</LastName><ForeName>A</ForeName><Initials>A</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2009</Year><Month>02</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>Ireland</Country><MedlineTA>Comput Methods Programs Biomed</MedlineTA><NlmUniqueID>8506513</NlmUniqueID><ISSNLinking>0169-2607</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001145" MajorTopicYN="N">Arrhythmias, Cardiac</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000503" MajorTopicYN="N">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001185" MajorTopicYN="N">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001331" MajorTopicYN="N">Automation</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002309" MajorTopicYN="N">Cardiology</DescriptorName><QualifierName UI="Q000295" MajorTopicYN="N">instrumentation</QualifierName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004562" MajorTopicYN="N">Electrocardiography</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010363" MajorTopicYN="N">Pattern Recognition, Automated</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012680" MajorTopicYN="N">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012815" MajorTopicYN="Y">Signal Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018879" MajorTopicYN="N">Ventricular Premature Complexes</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000503" MajorTopicYN="N">physiopathology</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2008</Year><Month>02</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2008</Year><Month>12</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2008</Year><Month>12</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2009</Year><Month>2</Month><Day>14</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2009</Year><Month>2</Month><Day>14</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2009</Year><Month>7</Month><Day>16</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">19215994</ArticleId><ArticleId IdType="pii">S0169-2607(09)00002-9</ArticleId><ArticleId IdType="doi">10.1016/j.cmpb.2008.12.009</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Algérie</li></country></list><tree><noCountry><name sortKey="Charef, A" sort="Charef, A" uniqKey="Charef A" first="A" last="Charef">A. Charef</name></noCountry><country name="Algérie"><noRegion><name sortKey="Talbi, M L" sort="Talbi, M L" uniqKey="Talbi M" first="M L" last="Talbi">M L Talbi</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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