A neuro-fuzzy identification of ECG beats.
Identifieur interne : 000109 ( Main/Exploration ); précédent : 000108; suivant : 000110A neuro-fuzzy identification of ECG beats.
Auteurs : Mohammed Amine Chikh [Algérie] ; Mohammed Ammar ; Radja MaroufSource :
- Journal of medical systems [ 0148-5598 ] ; 2012.
Descripteurs français
- KwdFr :
- MESH :
- diagnostic : Extrasystoles ventriculaires.
- méthodes : Traitement d'image par ordinateur.
- Humains, Logique floue, Sensibilité et spécificité, Électrocardiographie.
English descriptors
- KwdEn :
- MESH :
Abstract
This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC) beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also provides an acceptable accuracy. The performance of the classifier is tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. On the test set, we achieved an overall sensitivity and specificity of 97.92% and of 94.52% respectively. Experimental results show that the proposed approach is simple and effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level.
DOI: 10.1007/s10916-010-9554-4
PubMed: 20703643
Affiliations:
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Le document en format XML
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An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also provides an acceptable accuracy. The performance of the classifier is tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. On the test set, we achieved an overall sensitivity and specificity of 97.92% and of 94.52% respectively. Experimental results show that the proposed approach is simple and effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level.</div></front></TEI><affiliations><list><country><li>Algérie</li></country></list><tree><noCountry><name sortKey="Ammar, Mohammed" sort="Ammar, Mohammed" uniqKey="Ammar M" first="Mohammed" last="Ammar">Mohammed Ammar</name><name sortKey="Marouf, Radja" sort="Marouf, Radja" uniqKey="Marouf R" first="Radja" last="Marouf">Radja Marouf</name></noCountry><country name="Algérie"><noRegion><name sortKey="Chikh, Mohammed Amine" sort="Chikh, Mohammed Amine" uniqKey="Chikh M" first="Mohammed Amine" last="Chikh">Mohammed Amine Chikh</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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