title: Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves.
abstract: BACKGROUND
  There are limited studies on the automatic detection of T waves in arrhythmic electrocardiogram (ECG) signals. This is perhaps because there is no available arrhythmia dataset with annotated T waves. There is a growing need to develop numerically-efficient algorithms that can accommodate the new trend of battery-driven ECG devices. Moreover, there is also a need to analyze long-term recorded signals in a reliable and time-efficient manner, therefore improving the diagnostic ability of mobile devices and point-of-care technologies.
  
  
  METHODS
  Here, the T wave annotation of the well-known MIT-BIH arrhythmia database is discussed and provided. Moreover, a simple fast method for detecting T waves is introduced. A typical T wave detection method has been reduced to a basic approach consisting of two moving averages and dynamic thresholds. The dynamic thresholds were calibrated using four clinically known types of sinus node response to atrial premature depolarization (compensation, reset, interpolation, and reentry).
  
  
  RESULTS
  The determination of T wave peaks is performed and the proposed algorithm is evaluated on two well-known databases, the QT and MIT-BIH Arrhythmia databases. The detector obtained a sensitivity of 97.14% and a positive predictivity of 99.29% over the first lead of the validation databases (total of 221,186 beats).
  
  
  CONCLUSIONS
  We present a simple yet very reliable T wave detection algorithm that can be potentially implemented on mobile battery-driven devices. In contrast to complex methods, it can be easily implemented in a digital filter design.
authors.affiliation: Electrical and Computer Engineering in Medicine Group, University of British Columbia and BC Children's Hospital, Vancouver, BC V6H 3N1, Canada. moe.elgendi@gmail.com.|Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada. moe.elgendi@gmail.com.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Elgendi
authors.affiliation: Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuernbeg, Haberstr. 2, 91058 Erlangen, Germany. eskofier@cs.fau.de.
authors.forename: Bjoern
authors.initials: B
authors.lastname: Eskofier
authors.affiliation: School of Electrical and Electronic Engineering, University of Adelaide, Adelaide SA 5005, Australia. derek.abbott@adelaide.edu.au.
authors.forename: Derek
authors.initials: D
authors.lastname: Abbott
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000145:classification* / Q000379:methods; D006801:Humans; D012815:Signal Processing, Computer-Assisted
keywords: affordable healthcare; arrhythmia; moving averages