title: A fast noise-tolerant ECG feature recognition algorithm based on probabilistic analysis of gradient discontinuity.
abstract: Improvement in real-time electrocardiogram (ECG) interpretation is still needed, especially for QT estimation. This paper proposes a fast algorithm for ECG feature recognition, based on locating turning points in the waveform gradient.
  
  
  
  The algorithm places the fiducial point at the maximal value of a probabilistic decision function, assessing line intervals of best fit before and after the point and the point location relative to R-wave peaks already found.
  
  
  
  Fiducial points were successfully located for the 30 heartbeats annotated by a cardiologist of all 10 normal sinus rhythm records from the PhysioNet QT Database. For a given subject, the algorithm's QT estimation had superior repeatability, with intrasubject QT standard deviation just 5.42ms, 60% lower than the cardiologist's 13.57ms. Initial tests suggest immunity to noise of standard deviation up to about 9% of the signal, depending on noise type.
  
  
  
  The proposed algorithm is fast to calculate and noise-tolerant, and has shown improved repeatability in its QT estimation compared to a cardiologist.
authors.affiliation: Faculty of Engineering and Information Technology, University of Technology Sydney, Australia. Electronic address: Peter.Watterson@uts.edu.au.
authors.forename: Peter A
authors.initials: PA
authors.lastname: Watterson
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D004562:Electrocardiography; D006801:Humans; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted
keywords: Electrocardiography; Feature recognition; Fiducial points; QT interval estimation