OcrV1, Main, Exploration, bibRecord, 000F41

Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine

Identifieur interne : 000F41 ( Main/Exploration ); précédent : 000F40; suivant : 000F42

Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine

Auteurs : Kemal Polat [Turquie] ; Salih Günes [Turquie]

Source :

Applied mathematics and computation [ 0096-3003 ] ; 2007.

RBID : Pascal:07-0323669

Descripteurs français

Pascal (Inist)
- Equation différentielle, Système différentiel, Système expert, Méthode moindre carré, Homme, Apprentissage, Base donnée, Classification, Mathématiques appliquées, Analyse numérique, Détection, Approche système, Analyse composante principale, Rythme, Coeur, Période, Enregistrement, Diagnostic, Traitement, Classificateur, Californie, Informatique, Précision, Extrémité, Décision, Maladie, Courbe, 65Lxx, 53A45, SVM, Apprentissage machine, OCR, Analyse vectorielle.
Wicri :
- topic : Homme, Base de données, Classification, Informatique, Décision, Maladie.

English descriptors

KwdEn :
- Accuracy, Applied mathematics, California, Classification, Classifier, Computer science, Curve, Database, Decision, Detection, Diagnosis, Differential equation, Differential system, Disease, End, Expert system, Heart, Human, Learning, Least squares method, Numerical analysis, Period, Principal component analysis, Recording, Rhythm, System approach, Treatment, Vector analysis.

Abstract

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50-50% of training-test dataset, 70-30% of training-test dataset and 80-20% of training-test dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too.

Affiliations:

Turquie

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

to stream PascalFrancis, to step Corpus: 000344
to stream PascalFrancis, to step Curation: 000442
to stream PascalFrancis, to step Checkpoint: 000283
to stream Main, to step Merge: 000F54
to stream Main, to step Curation: 000F41

Le document en format XML

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<front><div type="abstract" xml:lang="en">Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50-50% of training-test dataset, 70-30% of training-test dataset and 80-20% of training-test dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too.</div>
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Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine

Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine

Source :

Descripteurs français

English descriptors

Abstract

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

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri