Practical Considerations for the Application of Nonlinear Indices Characterizing the Atrial Substrate in Atrial Fibrillation

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and in response to increasing clinical demand, a variety of signals and indices have been utilized for its analysis, which include complex fractionated atrial electrograms (CFAEs). New methodologies have been developed to characterize the atrial substrate, along with straightforward classification models to discriminate between paroxysmal and persistent AF (ParAF vs. PerAF). Yet, most previous works have missed the mark for the assessment of CFAE signal quality, as well as for studying their stability over time and between different recording locations. As a consequence, an atrial substrate assessment may be unreliable or inaccurate. The objectives of this work are, on the one hand, to make use of a reduced set of nonlinear indices that have been applied to CFAEs recorded from ParAF and PerAF patients to assess intra-recording and intra-patient stability and, on the other hand, to generate a simple classification model to discriminate between them. The dominant frequency (DF), AF cycle length, sample entropy (SE), and determinism (DET) of the Recurrence Quantification Analysis are the analyzed indices, along with the coefficient of variation (CV) which is utilized to indicate the corresponding alterations. The analysis of the intra-recording stability revealed that discarding noisy or artifacted CFAE segments provoked a significant variation in the CV(%) in any segment length for the DET and SE, with deeper decreases for longer segments. The intra-patient stability provided large variations in the CV(%) for the DET and even larger for the SE at any segment length. To discern ParAF versus PerAF, correlation matrix filters and Random Forests were employed, respectively, to remove redundant information and to rank the variables by relevance, while coarse tree models were built, optimally combining high-ranked indices, and tested with leave-one-out cross-validation. The best classification performance combined the SE and DF, with an accuracy (Acc) of 88.3%, to discriminate ParAF versus PerAF, while the highest single Acc was provided by the DET, reaching 82.2%. This work has demonstrated that due to the high variability of CFAEs data averaging from one recording place or among different recording places, as is traditionally made, it may lead to an unfair oversimplification of the CFAE-based atrial substrate characterization. Furthermore, a careful selection of reduced sets of features input to simple classification models is helpful to accurately discern the CFAEs of ParAF versus PerAF.

Characterization of the Substrate Modification in Patients Undergoing Catheter Ablation of Atrial Fibrillation.

“…While ECGs offer a generalized and universal perspective regarding the existence of remodeling, EGMs allow the precise localization of AF drivers from the study of various EGMs characteristics, such as low voltage and CFAEs that indicate the existence of fibrotic tissue [17,18,22,40]. Among the recruited EGM features, CFAEs have received increasing attention [18,40,179], as fractionation is thought to stem from the collision of multiple chaotic wavelets, hence being an indicator of fibrosis [110]. In fact, increased fractionation is connected with increased fibrosis, with CFAEs representing the existence of remodeling and being candidate CA targets [40].…”

“…One of the most common issues regarding CFAEs analysis is the confusion generated from their definition, which allows diverse interpretation, leading to overdetection and, therefore, to poor CA results and increased health risk for the individuals [40,43,48,56,108,179]. This observation led to the conduction of the first work described in this Doctoral Thesis, in chapter 3.…”