title: An arrhythmia classification system based on the RR-interval signal.
abstract: OBJECTIVE
  This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings.
  
  
  METHODS
  A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of beats: normal, premature ventricular contractions, ventricular flutter/fibrillation and 2 degrees heart block. The beat classification is used as input of a knowledge-based deterministic automaton to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricular trigeminy, ventricular couplet, ventricular tachycardia, ventricular flutter/fibrillation and 2 degrees heart block.
  
  
  RESULTS
  The method is evaluated by using the MIT-BIH arrhythmia database. The achieved scores indicate high performance: 98% accuracy for arrhythmic beat classification and 94% accuracy for arrhythmic episode detection and classification.
  
  
  CONCLUSIONS
  The proposed method is advantageous because it uses only the RR-interval signal for arrhythmia beat and episode classification and the results compare well with more complex methods.
authors.affiliation: Deparment of Computer Science, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina Campus, P.O. Box 1186, GR 45110 Ioannina, Greece.
authors.forename: M G
authors.initials: MG
authors.lastname: Tsipouras
authors.forename: D I
authors.initials: DI
authors.lastname: Fotiadis
authors.forename: D
authors.initials: D
authors.lastname: Sideris
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000145:classification* / Q000175:diagnosis; D001185:Artificial Intelligence; D002037:Bundle-Branch Block / Q000145:classification / Q000175:diagnosis; D019992:Databases as Topic; D004562:Electrocardiography / Q000145:classification*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D017180:Tachycardia, Ventricular / Q000145:classification / Q000175:diagnosis; D014693:Ventricular Fibrillation / Q000145:classification / Q000175:diagnosis; D018879:Ventricular Premature Complexes / Q000145:classification / Q000175:diagnosis