Dataset Information

Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation.

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 ? ? 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 % (? ? 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.

SUBMITTER: Almeida TP

PROVIDER: S-EPMC5069340 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation.

Almeida Tiago P TP Chu Gavin S GS Salinet João L JL Vanheusden Frederique J FJ Li Xin X Tuan Jiun H JH Stafford Peter J PJ Ng G André GA Schlindwein Fernando S FS

Medical & biological engineering & computing 20160225 11

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 perf ...[more]

PMID: 26914407

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Project description:Complex fractionated atrial electrograms (CFAEs) may represent a phenomenon associated with sources of atrial fibrillation (AF) and are being used increasingly as targets of catheter ablation. However, current methods have limited efficacy for characterizing CFAEs important to substrate arrhythmogenicity and do not measure electrogram morphology.The purpose of this study was to develop a methodology for quantifying the degree of morphologic heterogeneity in CFAE deflections, and to determine whether there are differences in this measurement between paroxysmal and persistent AF patients.Two successive bipolar CFAEs of length 8.4 seconds each were acquired during AF from two sites each at the ostia of the four pulmonary veins (PVs) and from the anterior and posterior left atrial free wall in patients with paroxysmal AF (N = 10) and long-standing persistent AF (N = 10). Extrinsic and intrinsic features of electrogram shape were used to characterize fractionation in CFAE sequences. The extrinsic parameters were the amplitude, upslope, downslope, and width of each deflection. The intrinsic parameter was the voltage profile as characterized by the sum of absolute values. These measurements were compared to the mean interval between CFAE deflections, a standard fractionation indicator.The variability of intrinsic/extrinsic morphologic parameters was higher in paroxysmal than persistent AF at the left superior PV (P < or =.003), the posterior left atrial free wall, anterior left atrial free wall, left inferior PV, and right superior PV (P <.05 for most parameters), and the right inferior PV (not significant). Mean CFAE deflection intervals were longer at all locations in paroxysmal AF but were significant only at the left superior PV and posterior left atrial free wall (P <.05). Quantitative morphologic parameters were not well correlated with dominant frequency (r(2) <0.32); thus, our new measures are robust to changes in activation rate.A novel method for quantifying CFAEs, independent of activation rate, has been developed. The method demonstrates greater significance in the difference between CFAE morphology in paroxysmal and long-standing AF compared with mean interval between CFAE deflections. The differences identified suggest that CFAE morphology may evolve as AF persists.

Project description:BACKGROUND:Electrogram-guided ablation procedures have been proposed as an alternative strategy consisting of either mapping and ablating focal sources or targeting complex fractionated electrograms in atrial fibrillation (AF). However, the incomplete understanding of the mechanism of AF makes difficult the decision of detecting the target sites. To date, feature extraction from electrograms is carried out mostly based on the time-domain morphology analysis and non-linear features. However, their combination has been reported to achieve better performance. Besides, most of the inferring approaches applied for identifying the levels of fractionation are supervised, which lack of an objective description of fractionation. This aspect complicates their application on EGM-guided ablation procedures. METHODS:This work proposes a semi-supervised clustering method of four levels of fractionation. In particular, we make use of the spectral clustering that groups a set of widely used features extracted from atrial electrograms. We also introduce a new atrial-deflection-based feature to quantify the fractionated activity. Further, based on the sequential forward selection, we find the optimal subset that provides the highest performance in terms of the cluster validation. The method is tested on external validation of a labeled database. The generalization ability of the proposed training approach is tested to aid semi-supervised learning on unlabeled dataset associated with anatomical information recorded from three patients. RESULTS:A joint set of four extracted features, based on two time-domain morphology analysis and two non-linear dynamics, are selected. To discriminate between four considered levels of fractionation, validation on a labeled database performs a suitable accuracy (77.6 %). Results show a congruence value of internal validation index among tested patients that is enough to reconstruct the patterns over the atria to located critical sites with the benefit of avoiding previous manual classification of AF types. CONCLUSIONS:To the best knowledge of the authors, this is the first work reporting semi-supervised clustering for distinguishing patterns in fractionated electrograms. The proposed methodology provides high performance for the detection of unknown patterns associated with critical EGM morphologies. Particularly, obtained results of semi-supervised training show the advantage of demanding fewer labeled data and less training time without significantly compromising accuracy. This paper introduces a new method, providing an objective scheme that enables electro-physiologist to recognize the diverse EGM morphologies reliably.

Dataset Information

Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation.

Publications

Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation.

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