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Adaptive rhythm sequencing: A method for dynamic rhythm classification during CPR
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DOI：10.1016/j.resuscitation.2015.02.031.png)
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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. Conclusion: In a continuous monitoring setting, adaptive rhythm sequencing was significantly more accurate than static rhythm classification during CPR. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
Keyword:
Cardiac arrest
Resuscitation
Cardiac rhythm
Hidden Markov model
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