Abstract:

This work investigates the impact of time constant offset in the body surface potential map (BSPM) on the recon- struction quality in electrocardiographic imaging (ECGI). For this purpose, a study comparing four different approaches for the reconstruction of the transmembrane voltage distribution (TMV) was carried out. From this four methods two of them were newly designed to estimate and remove the offset from the BSPM. The first approach uses a new formulation of the Tikhonov-Greensite method as augmented regularization to estimate and remove the time constant offset during the reconstruction. The second algorithm is related to classical signal processing. It applies a mode filter to remove the time constant offset in the BSPM and afterwards reconstructs the ventricular ectopic beat (VEB) using the Tikhonov-Greensite regularization. It can be shown that the time constant offset has a significant influence on the reconstruction quality and should be removed. The preferred method to remove time constant offset is the mode filter.

Abstract:

ECG imaging as noninvasive method is aiming to reconstruct the distribution of the transmembrane voltage amplitudes (TMVs) from the body surface potential map (BSPM). Due to the ill-posedness, standard approaches like the Tikhonov regularization method cause blurring and artefacts in the solution. To suppress blurring and artefacts, this work investigated a model based approach, the unscented Kalman filter (UKF). The intention of this paper is to show the potential of an UKF approach by using an idealized parametrization.

Abstract:

A common treatment of focal ventricular tachycardia is the catheter ablation of triggering sites. They have to be found manually by the physician during an intervention in a catheter lab. Thus, a method for determining the position of the focus automatically is desired. The inverse problem of electrocardiography addresses this problem by reconstructing the source of the ectopic beats using the surface ECG. This problem is ill-posed and therefore needs specific methods for solving it. We propose a machine learning approach for localisation of the ectopic foci in the heart to assist cardiologists with their therapy planning.We simulated 600 120-lead ECGs with different known excitation origins in the heart using a cellular automaton followed by a forward calculation. Features from the ECGs were used as input for a support vector regression (SVR). We assumed a functional relation between features from the ECG and the excitation origin. To benchmark SVR, we also used the well-known Tikhonov 0th order regularisation to reconstruct the transmembrane potentials in the heart and detect the location of the ectopic foci. Parameters for SVR and regularisation were chosen using a grid search minimising the error between estimated and true excitation origin. Compared to the Tikhonov regularisation method, SVR achieved a smaller deviation between estimated and real excitation origin evaluated with 6-fold cross validation. Future work could investigate on the behaviour on data from simulations with other torso and electrophysiological models, the influence of other methods for feature extraction and finally the evaluation with clinical data.

Abstract:

ECG imaging as a noninvasive technology has the aim to reconstruct the transmembrane potential distribution in the human ventricles based on the known body surface potential map. For patients suffering from ventricular ectopic beats (VEB), ECG imaging has also the task to localize the VEB origin.Besides the ill-posedness of ECG imaging, during the measurement in the catheter lab noise and signal artifacts can be occur. A very often signal corruption is time constant offset. It has a negative significant influence on the reconstruction and localization quality. In this work, we studied three different offset patterns, with two different quality criteria on two different time points. It can be shown that offset substracting methods arising either from the field of optimization or the field of signal processing can significantly improve the reconstruction and localization quality.In order to reduce the computational effort for ECG imaging, a machine learning algorithm was used to calculate directly the origin coordinates. This proof of concept will show that ECG imaging can be seen as a regression task and therefore solved with a support vector regression algorithm.