Classification of drug-induced hERG potassium-channel block from electrocardiographic T-wave features using artificial neural networks.

Journal: Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
PMID: 31347753

Abstract

BACKGROUND: Human ether-à-go-go-related gene (hERG) potassium-channel block represents a harmful side effect of drug therapy that may cause torsade de pointes (TdP). Analysis of ventricular repolarization through electrocardiographic T-wave features represents a noninvasive way to accurately evaluate the TdP risk in drug-safety studies. This study proposes an artificial neural network (ANN) for noninvasive electrocardiography-based classification of the hERG potassium-channel block.

Authors

  • Micaela Morettini
    Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
  • Chiara Peroni
    Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
  • Agnese Sbrollini
    Cardiology Department, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
  • Ilaria Marcantoni
    Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
  • Laura Burattini
    Information Engineering Department, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60121, Ancona, Italy.

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