The quantification of the QT-RR interaction in ECG signal using the detrended fluctuationanalysis and ARARX modelling.
Identifieur interne : 000244 ( Main/Exploration ); précédent : 000243; suivant : 000245The quantification of the QT-RR interaction in ECG signal using the detrended fluctuationanalysis and ARARX modelling.
Auteurs : Y N Baakek [France] ; Z E Hadj Slimane ; F. Bereksi ReguigSource :
- Journal of medical systems [ 1573-689X ] ; 2014.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Bloc de branche (anatomopathologie), Bloc de branche (diagnostic), Extrasystoles auriculaires (anatomopathologie), Extrasystoles auriculaires (diagnostic), Extrasystoles ventriculaires (anatomopathologie), Extrasystoles ventriculaires (diagnostic), Humains (MeSH), Traitement d'image par ordinateur (méthodes), Électrocardiographie (MeSH).
- MESH :
- anatomopathologie : Bloc de branche, Extrasystoles auriculaires, Extrasystoles ventriculaires.
- diagnostic : Bloc de branche, Extrasystoles auriculaires, Extrasystoles ventriculaires.
- méthodes : Traitement d'image par ordinateur.
- Algorithmes, Humains, Électrocardiographie.
English descriptors
- KwdEn :
- Algorithms (MeSH), Atrial Premature Complexes (diagnosis), Atrial Premature Complexes (pathology), Bundle-Branch Block (diagnosis), Bundle-Branch Block (pathology), Electrocardiography (MeSH), Humans (MeSH), Image Processing, Computer-Assisted (methods), Ventricular Premature Complexes (diagnosis), Ventricular Premature Complexes (pathology).
- MESH :
Abstract
In this paper, the detrended fluctuation analysis DFA is used to investigate and quantify the QT-RR interaction in different pathologic cases in order to distinguish between them. The study is carried out on the ECG signals of MIT-BIH universal database. Different ECG signals related to cardiac pathological cases are concerned with this study. These are: Premature Ventricular Contraction (PVC) (9 cases), Right Bundle Branch Block (RBBB) (4 cases), Left Bundle Branch Block (LBBB) (2 cases), Atrial Premature Beat (APB) (4 cases), Paced Beat (PB) (4 cases), and other pathologic cases with different severity (10 cases). All this cases are compared to the 15 normal cases. The obtained results show that the DFA can identify the healthy subject from the pathologic cases according to the values of the scaling exponent α. The results indicate that α varies between 0.5 and 1 in all cases which means that there is a long range correlation in RR and QT series. The QT and RR series are also modelled using the ARARX model. The parameters of the model are then extracted. The power spectral density (PSD) is estimated by using these parameters in order to provide further information about the causal interactions within the signals and also to determine the power scaling exponent β. This scaling exponent confirms the relationship between RR and QT intervals in all the studied cases except in APB and PB cases where the behaviour is similar to that of the white noise. The QT variability degrees are calculated and the DFA is applied on it. The obtained results show a long range correlation between RR and QT intervals in all cases and an ambiguity in the APB case. The DFA is compared to the Poincaré method in order to evaluate the algorithm performance using the Fuzzy Sugeno classifier is used for this purpose.
DOI: 10.1007/s10916-014-0062-9
PubMed: 24957388
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="2014">2014</date><idno type="RBID">pubmed:24957388</idno><idno type="pmid">24957388</idno><idno type="doi">10.1007/s10916-014-0062-9</idno><idno type="wicri:Area/PubMed/Corpus">000570</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000570</idno><idno type="wicri:Area/PubMed/Curation">000567</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000567</idno><idno type="wicri:Area/PubMed/Checkpoint">000559</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000559</idno><idno type="wicri:Area/Main/Merge">000244</idno><idno type="wicri:Area/Main/Curation">000244</idno><idno type="wicri:Area/Main/Exploration">000244</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">The quantification of the QT-RR interaction in ECG signal using the detrended fluctuationanalysis and ARARX modelling.</title><author><name sortKey="Baakek, Y N" sort="Baakek, Y N" uniqKey="Baakek Y" first="Y N" last="Baakek">Y N Baakek</name><affiliation wicri:level="3"><nlm:affiliation>Biomedical Engineering Laboratory (GBM) Electrical Engineering & Electronics Department, Faculty of Technology, Tlemcen University, B.P.119, Tlemcen, 13000, Algeria, baakek_nhy@yahoo.fr.</nlm:affiliation><country wicri:rule="url">France</country><wicri:regionArea>Biomedical Engineering Laboratory (GBM) Electrical Engineering & Electronics Department, Faculty of Technology, Tlemcen University, B.P.119, Tlemcen, 13000, Algeria</wicri:regionArea><placeName><region type="region" nuts="2">Provence-Alpes-Côte d'Azur</region><settlement type="city">Algeria</settlement></placeName></affiliation></author><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></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 systems</title><idno type="eISSN">1573-689X</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Atrial Premature Complexes (diagnosis)</term><term>Atrial Premature Complexes (pathology)</term><term>Bundle-Branch Block (diagnosis)</term><term>Bundle-Branch Block (pathology)</term><term>Electrocardiography (MeSH)</term><term>Humans (MeSH)</term><term>Image Processing, Computer-Assisted (methods)</term><term>Ventricular Premature Complexes (diagnosis)</term><term>Ventricular Premature Complexes (pathology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Bloc de branche (anatomopathologie)</term><term>Bloc de branche (diagnostic)</term><term>Extrasystoles auriculaires (anatomopathologie)</term><term>Extrasystoles auriculaires (diagnostic)</term><term>Extrasystoles ventriculaires (anatomopathologie)</term><term>Extrasystoles ventriculaires (diagnostic)</term><term>Humains (MeSH)</term><term>Traitement d'image par ordinateur (méthodes)</term><term>Électrocardiographie (MeSH)</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Bloc de branche</term><term>Extrasystoles auriculaires</term><term>Extrasystoles ventriculaires</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Atrial Premature Complexes</term><term>Bundle-Branch Block</term><term>Ventricular Premature Complexes</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Bloc de branche</term><term>Extrasystoles auriculaires</term><term>Extrasystoles ventriculaires</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Traitement d'image par ordinateur</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Atrial Premature Complexes</term><term>Bundle-Branch Block</term><term>Ventricular Premature Complexes</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Electrocardiography</term><term>Humans</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Humains</term><term>Électrocardiographie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, the detrended fluctuation analysis DFA is used to investigate and quantify the QT-RR interaction in different pathologic cases in order to distinguish between them. The study is carried out on the ECG signals of MIT-BIH universal database. Different ECG signals related to cardiac pathological cases are concerned with this study. These are: Premature Ventricular Contraction (PVC) (9 cases), Right Bundle Branch Block (RBBB) (4 cases), Left Bundle Branch Block (LBBB) (2 cases), Atrial Premature Beat (APB) (4 cases), Paced Beat (PB) (4 cases), and other pathologic cases with different severity (10 cases). All this cases are compared to the 15 normal cases. The obtained results show that the DFA can identify the healthy subject from the pathologic cases according to the values of the scaling exponent α. The results indicate that α varies between 0.5 and 1 in all cases which means that there is a long range correlation in RR and QT series. The QT and RR series are also modelled using the ARARX model. The parameters of the model are then extracted. The power spectral density (PSD) is estimated by using these parameters in order to provide further information about the causal interactions within the signals and also to determine the power scaling exponent β. This scaling exponent confirms the relationship between RR and QT intervals in all the studied cases except in APB and PB cases where the behaviour is similar to that of the white noise. The QT variability degrees are calculated and the DFA is applied on it. The obtained results show a long range correlation between RR and QT intervals in all cases and an ambiguity in the APB case. The DFA is compared to the Poincaré method in order to evaluate the algorithm performance using the Fuzzy Sugeno classifier is used for this purpose. </div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Provence-Alpes-Côte d'Azur</li></region><settlement><li>Algeria</li></settlement></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><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></noCountry><country name="France"><region name="Provence-Alpes-Côte d'Azur"><name sortKey="Baakek, Y N" sort="Baakek, Y N" uniqKey="Baakek Y" first="Y N" last="Baakek">Y N Baakek</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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