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ECG beat classification using particle swarm optimization and support vector machine

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Abstract

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.

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Authors and Affiliations

  1. Department of Electrical Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, 9417694686, Iran

    Ali Khazaee

  2. Faculty of Electrical and Computer Engineering, Babol University of Technology, Bobol, 4714871167, Iran

    A. E. Zadeh

Authors
  1. Ali Khazaee
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  2. A. E. Zadeh
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Corresponding author

Correspondence to Ali Khazaee.

Additional information

Ali Khazaee received the BS degree in Electronic Engineering from the Ferdowsi University, Mashhad, Iran, in 2007 and MS degree from the Babol University of Technology, Babol, Iran, in 2009. Currently, he is pursuing the PhD degree in the Department of Communication, Babol University of Technology, Babol, Iran. His research interests include biomedical signal processing and pattern recognition.

Ataollah Ebrahimzadeh received the PhD degree in Electrical and Computer Engineering. He is now an Associate Professor in the Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran. His current research interests include signal processing and artificial intelligence.

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Khazaee, A., Zadeh, A.E. ECG beat classification using particle swarm optimization and support vector machine. Front. Comput. Sci. 8, 217–231 (2014). https://doi.org/10.1007/s11704-014-2398-1

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