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Heartbeat Classification of ECG Signals Using Rational Function Systems

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Computer Aided Systems Theory – EUROCAST 2017 (EUROCAST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10672))

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Abstract

The main idea of this paper is to show that rational orthogonal function systems, called Malmquist-Takenaka (MT) systems can effectively be used for ECG heartbeat classification. The idea behind using these systems is the adaptive nature of them. Then the constructed feature vector consists of two main parts, called dynamic and morphological parameters. The latter ones contain the coefficients of the orthogonal projection with respect to the MT systems. Then Support Vector Machine algorithm was used for classifying the heartbeats into the usual 16 arrhythmia classes. The comparison test were performed on the MIT-BIH arrhythmia database. The results show that our algorithm outperforms the previous ones in many respects.

G. Bognár—Supported by the New National Excellence Program of the Ministry of Human Capacities of Hungary.

S. Fridli—This research was supported by the Hungarian Scientific Research Funds (OTKA) No. K115804.

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Authors and Affiliations

  1. Department of Numerical Analysis, Faculty of Informatics, ELTE Eötvös Loránd University, Budapest, Hungary

    Gergő Bognár & Sándor Fridli

Authors
  1. Gergő Bognár
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  2. Sándor Fridli
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Correspondence to Gergő Bognár .

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Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Bognár, G., Fridli, S. (2018). Heartbeat Classification of ECG Signals Using Rational Function Systems. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10672. Springer, Cham. https://doi.org/10.1007/978-3-319-74727-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-74727-9_22

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  • Online ISBN: 978-3-319-74727-9

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