AUTHOR=Yadan Zhang , Xin Lian , Jian Wu 

TITLE=Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

JOURNAL=Frontiers in Physiology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2022.999900

DOI=10.3389/fphys.2022.999900

ISSN=1664-042X

ABSTRACT=Electrocardiographic imaging (ECGI) can aid in identifying the lesion that causes and sustains atrial fibrillation. Traditional regularization strategies for addressing the ECGI inverse problem are not currently concerned about the multi-scale analysis of the inverse problem, and these techniques are not clinically reliable. We have previously investigated the solution based on uniform phase mode decomposition (UPEMD-based) to the ECGI inverse problem. Numerous other methods for the time-frequency analysis derived from empirical mode decomposition (EMD-based) have not been applied to the inverse problem in ECGI. By applying many EMD-based solutions to the ECGI inverse problem and evaluating the performance of these solutions, we hope to find a more efficient EMD-based solution to the ECGI inverse problem. 
In this study, five AF simulation datasets and two actual AF datasets are employed to evaluate the operating efficiency of several EMD-based solutions. The Pearson's correlation coefficient (CC), the relative difference measurement star (RDMS) of the computed epicardial dominant frequency (DF) map and driver probability (DP) map, and the Dis between the estimated and actual most probable lesion area (Dis) are used to evaluate the application of various EMD-based solutions in ECGI. The results demonstrate that the algorithm efficiency of the UPEMD-based solution and the solution based on the empirical wavelet transform (EWT-based) is significantly higher than that of the solutions based on multivariate mode decomposition, successive variation mode decomposition, multivariate empirical mode decomposition, noise-assisted multivariate empirical mode decomposition, and improved uniform phase mode decomposition. The UPEMD-based and EWT-based solutions’ Dis are much shorter, their CC are larger, and their RDMS are smaller than other solutions, demonstrating these two EMD-based solutions are superior and are suggested for clinical application in solving the ECGI inverse problem.