Detection of the QRS complex by linear prediction.
Identifieur interne : 000031 ( Main/Exploration ); précédent : 000030; suivant : 000032Detection of the QRS complex by linear prediction.
Auteurs : Z E Hadj Slimane [Algérie] ; F. Bereksi ReguigSource :
- Journal of medical engineering & technology [ 0309-1902 ]
Descripteurs français
- KwdFr :
- MESH :
- statistiques et données numériques : Électrocardiographie.
- Algorithmes, Humains, Interprétation statistique de données.
English descriptors
- KwdEn :
- MESH :
- statistics & numerical data : Electrocardiography.
- Algorithms, Data Interpretation, Statistical, Humans.
Abstract
The electrocardiogram (ECG) represents the electrical activity of the heart. It is characterized by its recurrent or periodic behaviour with each beat. Each recurrence is composed of a wave sequence consisting of P, QRS and T-waves, where the most characteristic wave set is the QRS complex. In this paper, we have developed an algorithm for detection of the QRS complex. The algorithm consists of several steps: signal-to-noise enhancement, linear prediction for ECG signal analysis, nonlinear transform, moving window integrator, centre-clipping transformation and QRS detection. Linear prediction determines the coefficients of a forward linear predictor by minimizing the prediction error by a least-square approach. The residual error signal obtained after processing by the linear prediction algorithm has very significant properties which will be used to localize and detect QRS complexes. The detection algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with the Pan and Tompkins QRS detection method. The results we obtain show that our method performs better than this method. Our algorithm results in fewer false positives and fewer false negatives.
DOI: 10.1080/03091900500286468
PubMed: 16772215
Affiliations:
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Le document en format XML
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Bereksi Reguig</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2006">2006 May-Jun</date><idno type="RBID">pubmed:16772215</idno><idno type="pmid">16772215</idno><idno type="doi">10.1080/03091900500286468</idno><idno type="wicri:Area/PubMed/Corpus">000795</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000795</idno><idno type="wicri:Area/PubMed/Curation">000791</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000791</idno><idno type="wicri:Area/PubMed/Checkpoint">000788</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000788</idno><idno type="wicri:Area/Main/Merge">000031</idno><idno type="wicri:Area/Main/Curation">000031</idno><idno type="wicri:Area/Main/Exploration">000031</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Detection of the QRS complex by linear prediction.</title><author><name sortKey="Hadj Slimane, Z E" sort="Hadj Slimane, Z E" uniqKey="Hadj Slimane Z" first="Z E" last="Hadj Slimane">Z E Hadj Slimane</name><affiliation wicri:level="1"><nlm:affiliation>Laboratoire de Génie Biomedical, Département d'électronique, Faculté des Sciences de l'Ingénieur, Université Abou Bekr Belkaid-Tlemcen. B.P.119, Tlemcen, 13000, Algeria. Hadjslim@yahoo.fr</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>Laboratoire de Génie Biomedical, Département d'électronique, Faculté des Sciences de l'Ingénieur, Université Abou Bekr Belkaid-Tlemcen. B.P.119, Tlemcen, 13000</wicri:regionArea><wicri:noRegion>13000</wicri:noRegion></affiliation></author><author><name sortKey="Bereksi Reguig, F" sort="Bereksi Reguig, F" uniqKey="Bereksi Reguig F" first="F" last="Bereksi Reguig">F. Bereksi Reguig</name></author></analytic><series><title level="j">Journal of medical engineering & technology</title><idno type="ISSN">0309-1902</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Data Interpretation, Statistical (MeSH)</term><term>Electrocardiography (statistics & numerical data)</term><term>Humans (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Humains (MeSH)</term><term>Interprétation statistique de données (MeSH)</term><term>Électrocardiographie (statistiques et données numériques)</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Electrocardiography</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Électrocardiographie</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Data Interpretation, Statistical</term><term>Humans</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Humains</term><term>Interprétation statistique de données</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The electrocardiogram (ECG) represents the electrical activity of the heart. It is characterized by its recurrent or periodic behaviour with each beat. Each recurrence is composed of a wave sequence consisting of P, QRS and T-waves, where the most characteristic wave set is the QRS complex. In this paper, we have developed an algorithm for detection of the QRS complex. The algorithm consists of several steps: signal-to-noise enhancement, linear prediction for ECG signal analysis, nonlinear transform, moving window integrator, centre-clipping transformation and QRS detection. Linear prediction determines the coefficients of a forward linear predictor by minimizing the prediction error by a least-square approach. The residual error signal obtained after processing by the linear prediction algorithm has very significant properties which will be used to localize and detect QRS complexes. The detection algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with the Pan and Tompkins QRS detection method. The results we obtain show that our method performs better than this method. Our algorithm results in fewer false positives and fewer false negatives.</div></front></TEI><affiliations><list><country><li>Algérie</li></country></list><tree><noCountry><name sortKey="Bereksi Reguig, F" sort="Bereksi Reguig, F" uniqKey="Bereksi Reguig F" first="F" last="Bereksi Reguig">F. Bereksi Reguig</name></noCountry><country name="Algérie"><noRegion><name sortKey="Hadj Slimane, Z E" sort="Hadj Slimane, Z E" uniqKey="Hadj Slimane Z" first="Z E" last="Hadj Slimane">Z E Hadj Slimane</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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