title: Adaptive rhythm sequencing: A method for dynamic rhythm classification during CPR.
abstract: OBJECTIVE
  The accuracy of methods that classify the cardiac rhythm despite CPR artifact could potentially be improved by utilizing continuous ECG data. Our objective is to compare three approaches which use identical ECG features and differ only in their degree of temporal integration: (1) static classification, which analyzes 4-s ECG frames in isolation; (2) "best-of-three averaging," which takes the average of three consecutive static classifications successively; and (3) "adaptive rhythm sequencing," which uses hidden Markov models to model ECG segments as rhythm sequences.
  
  
  METHODS
  Defibrillator recordings from 95 out-of-hospital cardiac arrests were divided into training and test sets. Each method classified the rhythm as asystole, organized rhythm or shockable rhythm throughout the recordings. Classifications were compared to the gold standard of physician review. The primary outcome was accuracy during CPR, which was estimated using a generalized linear mixed-effects model.
  
  
  RESULTS
  In the training set, accuracies during CPR were 0.89 (95% CI 0.85, 0.92), 0.92 (95% CI 0.89, 0.94) and 0.97 (95% CI 0.95, 0.98) for the static, best-of-three averaging and adaptive rhythm sequencing methods, respectively. The corresponding results in the test set were 0.92 (95% CI 0.86, 0.96), 0.94 (95% CI 0.89, 0.97), and 0.97 (95% CI 0.94, 0.99). Of the dynamic methods, only adaptive rhythm sequencing was significantly more accurate than static classification in the training (p < 0.001) and test (p = 0.03) sets.
  
  
  CONCLUSIONS
  In a continuous monitoring setting, adaptive rhythm sequencing was significantly more accurate than static rhythm classification during CPR.
authors: Center for Progress in Resuscitation, University of Washington, Seattle, WA, United States; Department of Medicine, University of Washington School of Medicine, Seattle, WA, United States. Electronic address: heemun@uw.edu.
authors: Heemun
authors: H
authors: Kwok
authors: Center for Progress in Resuscitation, University of Washington, Seattle, WA, United States; Department of Bioengineering, University of Washington, Seattle, WA, United States.
authors: Jason
authors: J
authors: Coult
authors: Department of Statistics, University of Washington, Seattle, WA, United States.
authors: Mathias
authors: M
authors: Drton
authors: Center for Progress in Resuscitation, University of Washington, Seattle, WA, United States; Department of Medicine, University of Washington School of Medicine, Seattle, WA, United States; King County Emergency Medical Services Division, Seattle King County Department of Public Health, Seattle, WA, United States.
authors: Thomas D
authors: TD
authors: Rea
authors: Center for Progress in Resuscitation, University of Washington, Seattle, WA, United States; Department of Medicine, University of Washington School of Medicine, Seattle, WA, United States; Department of Bioengineering, University of Washington, Seattle, WA, United States.
authors: Lawrence
authors: L
authors: Sherman
mesh_terms: D000368:Aged; D016887:Cardiopulmonary Resuscitation / Q000379:methods*; D047548:Defibrillators; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D058687:Out-of-Hospital Cardiac Arrest / Q000628:therapy*
keywords: Cardiac arrest; Cardiac rhythm; Hidden Markov model; Resuscitation