Minimizing discordances in automated classification of fractionated ...
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Minimizing discordances in automated classification of fractionated ...
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Med Biol Eng Comput DOI 10.1007/s11517-016-1456-2
ORIGINAL ARTICLE
Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation Tiago P. Almeida1 · Gavin S. Chu2,3 · João L. Salinet1,4 · Frederique J. Vanheusden1 · Xin Li1 · Jiun H. Tuan3 · Peter J. Stafford3 · G. André Ng2,3,5 · Fernando S. Schlindwein1,5
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: CFEMean ≤ 120 ms; CARTO: ICL ≥ 7], NavX classified 70 % of the electrograms as CFAEs, while CARTO detected
