Evaluation of five methods for the interpolation of bad leads in the solution of the inverse electrocardiography problem.

Objective.This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.Approach.We utilized experimental data from two distinct centers. Langendorff-perfused pig (n= 2) and dog (n= 2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.Main results.The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.Significance.This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.

The roles of mid-myocardial and epicardial cells in T-wave alternans development: a simulation study.

BACKGROUND: The occurrence of T-wave alternans in electrocardiographic signals was recently linked to susceptibility to ventricular arrhythmias and sudden cardiac death. Thus, by detecting and comprehending the origins of T-wave alternans, it might be possible to prevent such events. RESULTS: Here, we simulated T-wave alternans in a computer-generated human heart model by modulating the action potential duration and amplitude during the first part of the repolarization phase. We hypothesized that changes in the intracardiac alternans patterns of action potential properties would differentially influence T-wave alternans measurements at the body surface. Specifically, changes were simulated globally in the whole left and right ventricles to simulate concordant T-wave alternans, and locally in selected regions to simulate discordant and regional discordant, hereinafter referred to as "regional", T-wave alternans. Body surface potential maps and 12-lead electrocardiographic signals were then computed. In depth discrimination, the influence of epicardial layers on T-wave alternans development was significantly higher than that of mid-myocardial cells. Meanwhile, spatial discrimination revealed that discordant and regional action potential property changes had a higher influence on T-wave alternans amplitude than concordant changes. Notably, varying T-wave alternans sources yielded distinct body surface potential map patterns for T-wave alternans amplitude, which can be used for location of regions within hearts exhibiting impaired repolarization. The highest ability for T-wave alternans detection was achieved in lead V1. Ultimately, we proposed new parameters Vector Magnitude Alternans and Vector Angle Alternans, with higher ability for T-wave alternans detection when using multi-lead electrocardiographic signals processing than for single leads. Finally, QT alternans was found to be associated with the process of T-wave alternans generation. CONCLUSIONS: The distributions of the body surface T-wave alternans amplitude have been shown to have unique patterns depending on the type of alternans (concordant, discordant or regional) and the location of the disturbance in the heart. The influence of epicardial cells on T-wave alternans development is significantly higher than that of mid-myocardial cells, among which the sub-endocardial layer exerted the highest influence. QT interval alternans is identified as a phenomenon that correlate with T-wave alternans.

Effects of ECG Signal Processing on the Inverse Problem of Electrocardiography.

The inverse problem of electrocardiography is ill-posed. Errors in the model such as signal noise can impact the accuracy of reconstructed cardiac electrical activity. It is currently not known how sensitive the inverse problem is to signal processing techniques. To evaluate this, experimental data from a Langendorff-perfused pig heart (n=1) suspended in a human-shaped torso-tank was used. Different signal processing methods were applied to torso potentials recorded from 128 electrodes embedded in the tank surface. Processing methods were divided into three categories i) high-frequency noise removal ii) baseline drift removal and iii) signal averaging, culminating in n=72 different signal sets. For each signal set, the inverse problem was solved and reconstructed signals were compared to those directly recorded by the sock around the heart. ECG signal processing methods had a dramatic effect on reconstruction accuracy. In particular, removal of baseline drift significantly impacts the magnitude of reconstructed electrograms, while the presence of high-frequency noise impacts the activation time derived from these signals (p<0.05).

Reduction of Effects of Noise on the Inverse Problem of Electrocardiography with Bayesian Estimation.

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.

Noninvasive assessment of local myocardium repolarization changes using high resolution surface ECG mapping.

A method using body surface potential maps for assessment of myocardium lesions with changed repolarization is presented and suitable mapping system is introduced. Differences between normal and altered QRST integral maps together with torso volume conductor model were used to determine the equivalent dipole representing the lesion. Performance of the method was studied on simulated data. Changed repolarization was modeled by shortening of myocyte action potentials in regions typical for stenosis of the main coronary arteries. The equivalent dipole estimated the positions of small lesions with a mean error of 9+/-4 mm (17+/-14 mm for larger transmural lesions). The subepicardial or subendocardial character of the lesions was reflected in the dipole orientation. Tests of the method on patients after myocardial infarction that underwent coronary intervention on a single coronary vessel showed that in 7 of 8 successfully treated patients the dipole position matched well with the treated vessel. A small dipole moment in another patient indicated unsuccessful treatment. The method was implemented in a new 128-channel mapping system. Its active electrodes, battery powered measuring unit and optical computer interface help to minimize noise in ECG and guarantee patient's safety. The results suggest that the method and mapping system offer useful tools for noninvasive identification of local repolarization changes in the myocardium.

Model study of influence of extracardial factors on the inverse localization of preexcitation sites.

Inverse solution techniques are expected to help in noninvasive localization of ventricular preexcitation sites. The influence of selected extracardial factors on the accuracy of the inverse localization of the initial activation sites was studied on a model. Each of 8 simulated activation sequences was initiated in a different single starting point at the atrioventricular ring. Corresponding ecg potentials on the surface of a realistic model of inhomogeneous torso were used for the inverse localization procedure. A multiple dipole (MD) model of the cardiac generator composed of 39 segmental dipoles was used in the inverse computations. As it was shown in a previous study, the method was able to localize the 8 starting points even if a simplified torso model and a limited number of leads was used. In this study, influence of another two factors was evaluated: inaccuracy of location of the MD generator and presence of noise in surface potentials. Several shifts and rotations of the heart generator relative to its exact position were modeled. When the mean deviation of starting points was about 1 cm the mean localization error varied from 0.5 cm up to 1.0 cm for complete model data--198 surface potentials and a torso model including lungs and ventricular cavities. When a noise with uniform and Gaussian distribution was added to the surface potentials, the use of averaged body surface potentials significantly improved accuracy and stability of the inverse solution. For root mean square value of noise sigma = 14 microV the mean error of localization was 0.9 cm. For higher noise (sigma = 30 microV) the results were substantially deteriorated. The influence of a noise was studied on complete model data. (Tab. 3, Fig. 5. Ref. 6.)