Multifractal analysis for grading complex fractionated electrograms in atrial fibrillation

Abstract

Complex fractionated atrial electrograms provide an important tool for identifying arrhythmogenic substrates that can be used to guide catheter ablation for atrial fibrillation (AF). However, fractionation is a phenomenon that remains unclear. This paper aims to evaluate the multifractal properties of electrograms in AF in order to propose a method based on multifractal analysis able to discriminate between different levels of fractionation. We introduce a new method, the h-fluctuation index (hFI), where h is the generalised Hurst exponent, to extract information from the shape of the multifractal spectrum. Two multifractal frameworks are evaluated: multifractal detrended fluctuation analysis and wavelet transform modulus maxima. hFI is exemplified through its application in synthetic signals, and it is evaluated in a database of electrograms labeled on the basis of four degrees of fractionation. We compare the performance of hFI with other indexes, and find that hFI outperforms them. The results of the study provide evidence that multifractal analysis is useful for studying fractionation phenomena in AF electrograms, and indicate that hFI can be proposed as a tool for grade fractionation associated with the detection of target sites for ablation in AF.

Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach

“…In previous work, we have reported the use of ApEn to evaluate the location of rotors and block lines in a three-dimensional model of human atrial [100]; and the use of multifractal analysis as a tool to discriminate between four levels of fractionation according with a modified Well's approach [101]. In this work, we tested several nonlinear features using EGM signals recorded from a two-dimensional model of atrial tissue and a three-dimensional model of human atrial.…”

“…Fractal properties of physiological signals are not homogeneous, which means that local scaling properties change with time, and there is necessary to use different local Hurts exponent to describe the evolution of the system. Therefore, multifractal analysis could capture these changes in the global singularity distributions [101]. Then, the power law for multifractal behavior is written as follows: (4) where N α corresponds to the number of balls with a singular exponent equal to some value of α , necessary to cover a specific set, ϵ is the diameter of the balls that covered the set, and f ( α ) corresponds to the Hausdorff dimension.…”

“…[125]. Using f ( α ) , we calculate the h-fluctuation index as is described by Orozco-Duque et al [101].…”

Sudden Cardiac Death Prediction Based On a Nonlinear Estimation

“…The spectrum of singularities measures the global distribution of singularities with various regularities [12,19], and is related to the scaling exponent τ(q) via a Legendre transform, equation 3. where, the index q can take any real value except zero [20].…”

“…Multifractal detrend fluctuation analysis (MF-DFA) method proposed by [20], was used to calculated f (α). From the spectrum, determines the width (SW), which represents the degree of multifractality of the time series, the asymmetry index of the spectrum (AI) and the multifractal h-fluctuation index (hFI) is implemented and used in this work according with the parameters defined in [19]. The hFI extracts information for the shape of the singularity spectrum f (α) which is …”