Evolving neural networks using a genetic algorithm for heartbeat classification.
Identifieur interne : 000126 ( Main/Exploration ); précédent : 000125; suivant : 000127Evolving neural networks using a genetic algorithm for heartbeat classification.
Auteurs : Mansouria Sekkal [Algérie] ; Mohamed Amine Chikh ; Nesma SettoutiSource :
- Journal of medical engineering & technology [ 1464-522X ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
- diagnostic : Extrasystoles ventriculaires.
- Algorithmes, Humains, Rythme cardiaque.
English descriptors
- KwdEn :
- MESH :
- diagnosis : Ventricular Premature Complexes.
- Algorithms, Heart Rate, Humans, Neural Networks, Computer.
Abstract
This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared.
DOI: 10.3109/03091902.2011.574778
PubMed: 21574910
Affiliations:
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Le document en format XML
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As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared.</div></front></TEI><affiliations><list><country><li>Algérie</li></country></list><tree><noCountry><name sortKey="Chikh, Mohamed Amine" sort="Chikh, Mohamed Amine" uniqKey="Chikh M" first="Mohamed Amine" last="Chikh">Mohamed Amine Chikh</name><name sortKey="Settouti, Nesma" sort="Settouti, Nesma" uniqKey="Settouti N" first="Nesma" last="Settouti">Nesma Settouti</name></noCountry><country name="Algérie"><noRegion><name sortKey="Sekkal, Mansouria" sort="Sekkal, Mansouria" uniqKey="Sekkal M" first="Mansouria" last="Sekkal">Mansouria Sekkal</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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