Estimating Transmembrane Currents and Local Activation Times from Atrial Epicardial Electrograms

Abstract

Estimating the transmembrane currents travelling through the epicardium and local activation times based on atrial epicardial electrograms can greatly help in the study of cardiac arrhythmias such as atrial fibrillation. This work focuses on the accurate estimation of the aforementioned signals and features. To do this, two least squares-based regression methods were used to estimate transmembrane currents from electrograms and then find their local activation times by searching for the maximum negative slope. The first least squares optimization method consists of using standard least squares, while the second consists of regularized least squares, by combining both lasso and ridge regression, to deal with signal sparsity and multicollinearity, respectively. Furthermore, to improve estimation results, multiresolution analyses based on wavelet decompositions and principal components analysis were used to filter out parasitic components that were present in the estimated transmembrane currents by separating them from the main activation complex of the decomposed signals.
Using these algorithms on simulated data, it was shown that promising results can be achieved for both transmembrane current estimations and LAT estimations. Several wavelet support sizes were tested on the simulated data to observe performance changes. These were compared to an already existing LAT estimation algorithm. The results mainly confirm the efficiency of the proposed methods on severely diseased tissue corrupted by conduction blocks and noise.