MaghrebDataLibMedV2, PubMed, Checkpoint, bibRecord, 000676

A neuro-fuzzy identification of ECG beats.

Identifieur interne : 000676 ( PubMed/Checkpoint ); précédent : 000675; suivant : 000677

A neuro-fuzzy identification of ECG beats.

Auteurs : Mohammed Amine Chikh [Algérie] ; Mohammed Ammar ; Radja Marouf

Source :

Journal of medical systems [ 0148-5598 ] ; 2012.

RBID : pubmed:20703643

Descripteurs français

KwdFr :
- Extrasystoles ventriculaires (diagnostic), Humains (MeSH), Logique floue (MeSH), Sensibilité et spécificité (MeSH), Traitement d'image par ordinateur (méthodes), Électrocardiographie (MeSH).
MESH :
- diagnostic : Extrasystoles ventriculaires.
- méthodes : Traitement d'image par ordinateur.
- Humains, Logique floue, Sensibilité et spécificité, Électrocardiographie.

English descriptors

KwdEn :
- Electrocardiography (MeSH), Fuzzy Logic (MeSH), Humans (MeSH), Image Processing, Computer-Assisted (methods), Neural Networks, Computer (MeSH), Sensitivity and Specificity (MeSH), Ventricular Premature Complexes (diagnosis).
MESH :
- diagnosis : Ventricular Premature Complexes.
- methods : Image Processing, Computer-Assisted.
- Electrocardiography, Fuzzy Logic, Humans, Neural Networks, Computer, Sensitivity and Specificity.

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:

Algérie

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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A neuro-fuzzy identification of ECG beats.

A neuro-fuzzy identification of ECG beats.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki