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Title: Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation
Authors: Almeida, Tiago P.
Chu, Gavin S.
Salinet, J. L.
Vanheusden, Frederique J.
Li, Xin
Tuan, Jiun H.
Stafford, Peter J.
Ng, G. André
Schlindwein, Fernando S.
First Published: 25-Feb-2016
Citation: Medical & Biological Engineering & Computing, 2016, doi: 10.1007/s11517-016-1456-2
Abstract: Ablation of persistent atrial fibrillation (persAF) targeting complex fractionated atrial electrograms (CFAEs) detected by automated algorithms has produced conflicting outcomes in previous electrophysiological studies. We hypothesize that the differences in these algorithms could lead to discordant CFAE classifications by the available mapping systems, giving rise to potential disparities in CFAE-guided ablation. This study reports the results of a head-to-head comparison of CFAE detection performed by NavX (St. Jude Medical) versus CARTO (Biosense Webster) on the same bipolar electrogram data (797 electrograms) from 18 persAF patients. We propose revised thresholds for both primary and complementary indices to minimize the differences in CFAE classification performed by either system. Using the default thresholds [NavX: CFE-Mean ≤ 120 ms; CARTO: ICL ≥ 7], NavX classified 70 % of the electrograms as CFAEs, while CARTO detected 36 % (Cohen’s kappa κ ≈ 0.3, P < 0.0001). Using revised thresholds found using receiver operating characteristic curves [NavX: CFE-Mean ≤ 84 ms, CFE-SD ≤ 47 ms; CARTO: ICL ≥ 4, ACI ≤ 82 ms, SCI ≤ 58 ms], NavX classified 45 %, while CARTO detected 42 % (κ ≈ 0.5, P < 0.0001). Our results show that CFAE target identification is dependent on the system and thresholds used by the electrophysiological study. The thresholds found in this work counterbalance the differences in automated CFAE classification performed by each system. This could facilitate comparisons of CFAE ablation outcomes guided by either NavX or CARTO in future works.
DOI Link: 10.1007/s11517-016-1456-2
ISSN: 0140-0118
eISSN: 1741-0444
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2016. This is an open-access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Engineering

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