Fractional wavelet for R-Wave detection in ECG signal.
Identifieur interne : 000089 ( Main/Merge ); précédent : 000088; suivant : 000090Fractional wavelet for R-Wave detection in ECG signal.
Auteurs : A. Charef [Algérie] ; F. AbdellicheSource :
- Critical reviews in biomedical engineering [ 0278-940X ] ; 2008.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Diagnostic assisté par ordinateur (méthodes), Humains (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), Électrocardiographie (méthodes).
- MESH :
English descriptors
- KwdEn :
- MESH :
Abstract
In this paper, we propose a method for the R-wave detection of the QRS complex of the ECG signal using a fractional wavelet. This fractional wavelet is defined, in this context, as the second derivative of the Cole-Cole distribution function of relaxation times, which is widely used in mathematical modeling of fractional order systems. In order to reduce the noise level for the ECG data, our detection algorithm incorporates the proposed fractional wavelet whose parameters are obtained by maximizing the signal-to-noise ratio of the ECG. The scales of this wavelet are chosen based on the spectral characteristics of the ECG. The proposed algorithm performances were evaluated using the MIT-BIH arrhythmia database. The numerical results show that the proposed algorithm achieved a detection rate of about 99.56%.
DOI: 10.1615/critrevbiomedeng.v36.i2-3.10
PubMed: 19740068
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pubmed:19740068Le document en format XML
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Charef</name><affiliation wicri:level="1"><nlm:affiliation>Département d'Electronique, Université Mentouri, Route Ain El Bey, Constantine, Algeria. abdelliche_faycal@yahoo.fr</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>Département d'Electronique, Université Mentouri, Route Ain El Bey, Constantine</wicri:regionArea><wicri:noRegion>Constantine</wicri:noRegion></affiliation></author><author><name sortKey="Abdelliche, F" sort="Abdelliche, F" uniqKey="Abdelliche F" first="F" last="Abdelliche">F. 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This fractional wavelet is defined, in this context, as the second derivative of the Cole-Cole distribution function of relaxation times, which is widely used in mathematical modeling of fractional order systems. In order to reduce the noise level for the ECG data, our detection algorithm incorporates the proposed fractional wavelet whose parameters are obtained by maximizing the signal-to-noise ratio of the ECG. The scales of this wavelet are chosen based on the spectral characteristics of the ECG. The proposed algorithm performances were evaluated using the MIT-BIH arrhythmia database. The numerical results show that the proposed algorithm achieved a detection rate of about 99.56%.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19740068</PMID><DateCompleted><Year>2009</Year><Month>12</Month><Day>01</Day></DateCompleted><DateRevised><Year>2019</Year><Month>11</Month><Day>11</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0278-940X</ISSN><JournalIssue CitedMedium="Print"><Volume>36</Volume><Issue>2-3</Issue><PubDate><Year>2008</Year></PubDate></JournalIssue><Title>Critical reviews in biomedical engineering</Title><ISOAbbreviation>Crit Rev Biomed Eng</ISOAbbreviation></Journal><ArticleTitle>Fractional wavelet for R-Wave detection in ECG signal.</ArticleTitle><Pagination><MedlinePgn>79-91</MedlinePgn></Pagination><Abstract><AbstractText>In this paper, we propose a method for the R-wave detection of the QRS complex of the ECG signal using a fractional wavelet. This fractional wavelet is defined, in this context, as the second derivative of the Cole-Cole distribution function of relaxation times, which is widely used in mathematical modeling of fractional order systems. In order to reduce the noise level for the ECG data, our detection algorithm incorporates the proposed fractional wavelet whose parameters are obtained by maximizing the signal-to-noise ratio of the ECG. The scales of this wavelet are chosen based on the spectral characteristics of the ECG. The proposed algorithm performances were evaluated using the MIT-BIH arrhythmia database. The numerical results show that the proposed algorithm achieved a detection rate of about 99.56%.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Charef</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Département d'Electronique, Université Mentouri, Route Ain El Bey, Constantine, Algeria. abdelliche_faycal@yahoo.fr</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Abdelliche</LastName><ForeName>F</ForeName><Initials>F</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Crit Rev Biomed Eng</MedlineTA><NlmUniqueID>8208627</NlmUniqueID><ISSNLinking>0278-940X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001145" MajorTopicYN="N">Arrhythmias, Cardiac</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003936" MajorTopicYN="N">Diagnosis, Computer-Assisted</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">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="D010363" MajorTopicYN="N">Pattern Recognition, Automated</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">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></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2009</Year><Month>9</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2008</Year><Month>1</Month><Day>1</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2009</Year><Month>12</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">19740068</ArticleId><ArticleId IdType="pii">19160f1c0f24e5ff,7c258a5373d63856</ArticleId><ArticleId IdType="doi">10.1615/critrevbiomedeng.v36.i2-3.10</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Algérie</li></country></list><tree><noCountry><name sortKey="Abdelliche, F" sort="Abdelliche, F" uniqKey="Abdelliche F" first="F" last="Abdelliche">F. Abdelliche</name></noCountry><country name="Algérie"><noRegion><name sortKey="Charef, A" sort="Charef, A" uniqKey="Charef A" first="A" last="Charef">A. Charef</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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