Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Journal: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
PMID: 30476061

Abstract

AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from 12-lead electrocardiograms (ECGs) in diagnosing LQTS in a large cohort of gene-positive LQTS patients and gene-negative family members using a support vector machine.

Authors

  • Ben J M Hermans
    Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.
  • Job Stoks
    Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.
  • Frank C Bennis
    Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, Netherlands. MHeNS School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, Netherlands.
  • Arja S Vink
    Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, DD Amsterdam, the Netherlands.
  • Ainara Garde
    Department of Biomedical Signals and Systems, Faculty EEMCS, University of Twente, AE Enschede, The Netherlands.
  • Arthur A M Wilde
    European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart: ERN GUARD-Heart (P.D.L., A.A.M.W.).
  • Laurent Pison
    Department of Cardiology, Maastricht University Medical Centre, AZ Maastricht, The Netherlands.
  • Pieter G Postema
    Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, DD Amsterdam, the Netherlands.
  • Tammo Delhaas

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