title: Validation of automatic measurement of QT interval variability.
abstract: BACKGROUND
  Increased variability of beat-to-beat QT-interval durations on the electrocardiogram (ECG) has been associated with increased risk for fatal and non-fatal cardiac events. However, techniques for the measurement of QT variability (QTV) have not been validated since a gold standard is not available. In this study, we propose a validation method and illustrate its use for the validation of two automatic QTV measurement techniques.
  
  
  METHODS
  Our method generates artificial standard 12-lead ECGs based on the averaged P-QRS-T complexes from a variety of existing ECG signals, with simulated intrinsic (QT interval) and extrinsic (noise, baseline wander, signal length) variations. We quantified QTV by a commonly used measure, short-term QT variability (STV). Using 28,800 simulated ECGs, we assessed the performance of a conventional QTV measurement algorithm, resembling a manual QTV measurement approach, and a more advanced algorithm based on fiducial segment averaging (FSA).
  
  
  RESULTS
  The results for the conventional algorithm show considerable median absolute differences between the simulated and estimated STV. For the highest noise level, median differences were 4-6 ms in the absence of QTV. Increasing signal length generally yields more accurate STV estimates, but the difference in performance between 30 or 60 beats is small. The FSA algorithm proved to be very accurate, with most median absolute differences less than 0.5 ms, even for the highest levels of disturbance.
  
  
  CONCLUSIONS
  Artificially constructed ECGs with a variety of disturbances allow validation of QTV measurement procedures. The FSA algorithm provides highly accurate STV estimates under varying signal conditions, and performs much better than traditional beat-by-beat analysis. The fully automatic operation of the FSA algorithm enables STV measurement in large sets of ECGs.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
authors.forename: Peter R
authors.initials: PR
authors.lastname: Rijnbeek
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
authors.forename: Marten E
authors.initials: ME
authors.lastname: van den Berg
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
authors.forename: Gerard
authors.initials: G
authors.lastname: van Herpen
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
authors.forename: Henk J
authors.initials: HJ
authors.lastname: Ritsema van Eck
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Kors
mesh_terms: D000465:Algorithms; D004562:Electrocardiography; D006331:Heart Diseases / Q000175:diagnosis*; D006339:Heart Rate; D006801:Humans; D009200:Myocardial Contraction; D012815:Signal Processing, Computer-Assisted