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

Ben J. M. Hermans, Job Stoks, Frank C. Bennis, Arja S. Vink, Ainara Garde, Arthur A. M. Wilde, Laurent Pison, Pieter G. Postema, Tammo Delhaas*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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 genepositive LQTS patients and gene-negative family members using a support vector machine.

Methods and results A retrospective study was performed including 688 digital 12-lead ECGs recorded from genotype-positive LQTS patients and genotype-negative relatives at their first visit. Two models were trained and tested equally: a baseline model with age, gender, RR-interval, QT-interval, and QTc-intervals as inputs and an extended model including morphology features as well. The best performing baseline model showed an area under the receiver-operating characteristic curve (AUC) of 0.821, whereas the extended model showed an AUC of 0.901. Sensitivity and specificity at the maximal Youden's indexes changed from 0.694 and 0.829 with the baseline model to 0.820 and 0.861 with the extended model. Compared with clinically used QTc-interval cut-off values (> 480 ms), the extended model showed a major drop in false negative classifications of LQTS patients.

Conclusion The support vector machine-based extended model with T-wave morphology markers resulted in a major rise in sensitivity and specificity at the maximal Youden's index. From this, it can be concluded that T-wave morphology assessment has an added value in the diagnosis of LQTS.

Original languageEnglish
Pages (from-to)113-119
Number of pages7
JournalEP Europace
Volume20
DOIs
Publication statusPublished - Nov 2018
Event9th Theo Rossi di Montelera (TRM) Forum on Computer Simulation and Experimental Assessment of Cardiac Function: From Model to Clinical Outcome - Ctr Computat Med Cardiol, Lugano, SWITZERLAND, Lugano, Switzerland
Duration: 4 Dec 20175 Dec 2017

Keywords

  • QT-interval
  • T-wave
  • Morphology
  • Long QT syndrome
  • Machine learning
  • INTERVAL
  • REGULARIZATION

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