title: Validation of a novel algorithm for quantification of the percentage of signal fractionation in atrial fibrillation.
abstract: OBJECTIVE
  Catheter ablation for paroxysmal atrial fibrillation (AF) is rapidly becoming a standard practice. There is literature to support that catheter ablation of persistent AF requires additional 'substrate modification'. In clinical practice, operators rely on automated fractionation maps created by three-dimensional anatomic mapping systems to rapidly assess complex 'fractionated' signals (CFAE). These systems use differing algorithms to automate the process. The agreement between operators and contemporary algorithms has not been examined. We sought to assess the agreement between operators and a novel method of quantification calculating percentage fractionation (PF).
  
  
  RESULTS
  Expert opinion on 80 atrial electrogram 4 s signals of varying levels of activity were gathered and pooled for comparison. Twelve independent experts visually quantified the signal fractionation and offered a threshold level for ablation. We developed an algorithm to find sites with high continuous electrical activity, or high PF. Correlation between experts and PF was 0.78 [P < 0.01, 95% confidence interval (CI) (0.68-0.86)]. Receiver operating characteristics curve sensitivity and specificity for PF were 0.7727 and 0.8103 at the optimal cut-off point of 58.45 PF with area under curve 0.89 CI (0.80-0.99).
  
  
  CONCLUSIONS
  The PF statistic represents a more robust and intuitive measure to represent fractionated atrial activity; importantly it demonstrates excellent agreement with expert users and presents a new standard for algorithm assessment. Use of a PF statistic should be considered in automated mapping systems.
authors.affiliation: Division of Cardiology (Heart Rhythm Service), Mathematics and Statistics Department, Faculty of Medicine,  Kingston General Hospital, School of Computing, Queen's University, Kingston, ON K7L 2V7,  Canada.
authors.forename: Charlotte L
authors.initials: CL
authors.lastname: Haley
authors.forename: Lorne J
authors.initials: LJ
authors.lastname: Gula
authors.forename: Rodrigo
authors.initials: R
authors.lastname: Miranda
authors.forename: Kevin A
authors.initials: KA
authors.lastname: Michael
authors.forename: Adrian M
authors.initials: AM
authors.lastname: Baranchuk
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Simpson
authors.forename: Hoshiar
authors.initials: H
authors.lastname: Abdollah
authors.forename: Adam J
authors.initials: AJ
authors.lastname: West
authors.forename: Selim G
authors.initials: SG
authors.lastname: Akl
authors.forename: Damian P
authors.initials: DP
authors.lastname: Redfearn
mesh_terms: D000368:Aged; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology / Q000601:surgery; D001331:Automation; D017115:Catheter Ablation; D022062:Electrophysiologic Techniques, Cardiac; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted