ECG beat classification using particle swarm optimization and support vector machine
Ali KHAZAEE1,*(),A. E. ZADEH2
1. Department of Electrical Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd 9417694686, Iran 2. Faculty of Electrical and Computer Engineering, Babol University of Technology, Bobol 4714871167, Iran
In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine (SVM) classifier. The method extracts electrocardiogram’s spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using particle swarm optimization (PSO). These parameters are: Gaussian radial basis function (GRBF) kernel parameter σ and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid particle swarm optimization-support vector machine (SVMPSO) classification method offers significantly improved performance over the SVM which has constant and manually extracted parameter.
. [J]. Frontiers of Computer Science, 2014, 8(2): 217-231.
Ali KHAZAEE,A. E. ZADEH. ECG beat classification using particle swarm optimization and support vector machine. Front. Comput. Sci., 2014, 8(2): 217-231.
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