title: Robust QT interval estimation--from algorithm to validation.
abstract: BACKGROUND
  This article presents an effort of measuring QT interval with automatic computerized algorithms. The aims of the algorithms are consistency as well as accuracy. Multilead and multibeat information from a given segment of ECG are used for more consistent QT interval measurement.
  
  
  METHODS
  A representative beat is generated from selected segment of each lead, and then a composite beat is formed by the representative beats of all independent leads. The end result of the QT measure is so-called global QT measurement, which usually correlates with the longest QT interval in multiple leads. Individual lead QT interval was estimated by using the global measurement as a starting point, and then adapted to the signal of the particular lead and beat. In general, beat-by-beat QT measurement is more prone to noise, therefore less reliable than the global estimation. It is usually difficult to know if difference of beat-by-beat QT interval is due to true physiological change or noise fluctuation.
  
  
  RESULTS
  The algorithm was tested independently by a clinical database. It is also tested against action potential duration (APD) generated by a Cell-to-ECG forward-modeling based simulation signals. The modeling approach provided an objective test for the QT estimation. The modeling approach allowed us to evaluate the QT measurement versus APD. The mean error between the algorithm and cardiologist QT intervals is 3.95 +/- 5.5 ms, based on the large clinical trial database consisting of 15,910 ECGs. The mean error between QT intervals and maximum APD is 17 +/- 2.4, and the correlation coefficient is 0.99.
  
  
  CONCLUSIONS
  The global QT interval measurement method presented in this study shows very satisfactory results against the CSE database and a large clinical trial database. The modeling test approach used in this study provides an alternative "gold standard" for QT interval measurement.
authors.affiliation: GE Healthcare, Milwaukee, Wisconsin, USA. joel.xue@med.ge.com
authors.forename: Joel Q
authors.initials: JQ
authors.lastname: Xue
mesh_terms: D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted