Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 27, 2018

Abstract

BACKGROUND AND OBJECTIVE: Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computational tools can provide essential assistance to specialists. This paper presents a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes (Q, R and S waves).

Authors

  • Bruno Rodrigues de Oliveira
    Department of Electrical Engineering, São Paulo State University (UNESP), Ilha Solteira, Brazil. Electronic address: bruno@cerradosites.com.
  • Caio Cesar Enside de Abreu
    Department of Computing, Mato Grosso State University (UNEMAT), Alto Araguaia, Brazil. Electronic address: caio@unemat.br.
  • Marco Aparecido Queiroz Duarte
    Department of Mathematics, Mato Grosso do Sul State University (UEMS), Cassilândia, Brazil. Electronic address: marco@uems.br.
  • Jozue Vieira Filho
    Telecommunication and Aeronautic Engineering, São Paulo State University (UNESP), São João da Boa Vista, Brazil. Electronic address: jozue.vieira@unesp.br.

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