International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 5, No. 6, 8 May 2013
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Heart Beat Classification Using Particle Swarm Optimization
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Author(s)
Ali Khazaee
1,*
Index Terms
ECG Beat Classification, SVM, PSO, Feature Selection
Abstract
This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.
Cite This Paper
Ali Khazaee, "Heart Beat Classification Using Particle Swarm Optimization", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.6, pp.25-33, 2013. DOI:10.5815/ijisa.2013.06.03
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