Reduction of Effects of Noise on the Inverse Problem of Electrocardiography With Bayesian Estimation

Abstract

To overcome the ill-posed nature of the inverse problem of electrocardiography (ECG) and stabilize the solutions, regularization is used. Despite several studies on noise, effect of prefiltering of ECG signals on the regularized inverse solutions has not been explored. We used Bayesian estimation for solving the inverse ECG problem with and without applying various prefiltering methods, and evaluated our results using experimental data that came from a Langendorff-perfused pig heart suspended in a human-shaped torso-tank. Epicardial electrograms were recorded during RV pacing using a 108-electrode array, simultaneously with ECGs from 128 electrodes embedded in the tank surface. Leave-one-beat-out protocol was used to obtain the prior probability density function (pdf) of electro-grams and noise statistics. Noise pdf was assumed to be zero mean-Gaussian, with covariance assumptions: a) independent and identically distributed (noi-iid), b) correlated (noi-corr). Reconstructed electrograms and activation times were compared to those directly recorded by the sock for 3 beats selected from the recording. Noi-corr is superior to noi-iid when the training set is a good match to data, but for applications requiring activation time derivation, careful selection of preprocessing methods, in particular to adequately remove high-frequency noise, and an appropriate noise model is needed.