T-wave end detection using neural networks and Support Vector Machines.

Journal: Computers in biology and medicine
Published Date: Mar 6, 2018

Abstract

BACKGROUND AND OBJECTIVE: In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines.

Authors

  • Alexander Alexeis Suárez-León
    Universidad de Oriente, Faculty of Telecommunications, Informatics and Biomedical Engineering, Santiago de Cuba, Cuba; KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium. Electronic address: aasl@uo.edu.cu.
  • Carolina Varon
    Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics and iMinds Medical IT Department, KU Leuven, Leuven, Belgium.
  • Rik Willems
    KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium; UZ Leuven, Leuven, Belgium. Electronic address: rik.willems@kuleuven.be.
  • Sabine Van Huffel
    Katholieke Universiteit Leuven.
  • Carlos Román Vázquez-Seisdedos
    Universidad de Oriente, Faculty of Telecommunications, Informatics and Biomedical Engineering, Santiago de Cuba, Cuba. Electronic address: cvazquez@uo.edu.cu.

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