Electrocardiographic imaging (ECGI) reconstructs epicardial potentials and electrograms from body-surface electrocardiograms and a torso-heart geometry. For clinical purposes, local activation and recovery times are often more useful than epicardial electrograms. However, noise and fractionation can affect estimation of activation and recovery times from reconstructed electrograms. Here, we employ a method for activation and recovery time estimation that detects the simultaneous presence of spatial and temporal features associated with a passing wavefront and evaluate this in a series of canine experiments. We show that estimation of activation times is more accurate when this spatiotemporal approach is used, however, recovery times are best determined with a temporal-only approach. Additional spatial smoothing further benefits activation and recovery time estimation in all cases. This results in a median beat origin localization error of only one centimeter, which could expedite catheter-based diagnostic evaluation and ablation in clinical settings.
Find the paper here, and don’t hesitate to contact us with your ideas and suggestions!
Reference: Matthijs Cluitmans, Jaume Coll-Font, Burak Erem, Dana Brooks, Pietro Bonizzi, Joël Karel, Paul Volders, Ralf Peeters and Ronald Westra. Spatiotemporal Activation Time Estimation Improves Noninvasive Localization of Cardiac Electrical Activity. In Computing in Cardiology, 2016.
Noninvasive imaging of electrical activity of the heart has increasingly gained attention over the last decades. Epicardial potentials can be reconstructed from a torso-heart geometry and body-surface potentials recorded from tens to hundreds of body-surface electrodes. However, it remains an open question how many body-surface electrodes are needed to accurately reconstruct epicardial potentials. We investigated the influence of the number of body-surface electrodes in an in vivo experiment. Find the paper here, and don’t hesitate to contact us with your ideas and suggestions!
Reference: Matthijs Cluitmans, Joël Karel, Pietro Bonizzi, Monique de Jong, Paul Volders, Ralf Peeters and Ronald Westra. In-vivo Evaluation of Reduced-Lead-Systems in Noninvasive Reconstruction and Localization of Cardiac Electrical Activity. In Computing in Cardiology, 2015.
In this research, we have improved our method to noninvasively reconstruct electrical heart activity by using physiology-inspired building blocks and directly reconstructing the heart’s activity in terms of those building blocks. This method was validated with unique in vivo data. Find the (award winning) paper here, and don’t hesitate to contact us with your ideas and suggestions!
Reference: Matthijs JM Cluitmans, Monique MJ de Jong, Paul GA Volders, Ralf LM Peeters and Ronald L Westra. Physiology-based Regularization Improves Noninvasive Reconstruction and Localization of Cardiac Electrical Activity. In Computing in Cardiology, 2014.
At the Computing in Cardiology conference, we presented two ideas that seem to improve the inverse reconstruction of electrical heart activity. In the first, we propose to use a (computer generated) training set of realistic heart activity as building blocks for reconstructed electrograms at the heart surface. The second idea is to improve inversely reconstructed electrograms by matching their vectorcardiographic characteristics from those observed at the body surface. Find the (award winning) poster below and the paper here, and don’t hesitate to contact us with your ideas and suggestions!
Reference: Matthijs JM Cluitmans, Pietro Bonizzi, Joel MH Karel, Paul GA Volders, Ralf LM Peeters, and Ronald L Westra. Inverse reconstruction of epicardial potentials improves by vectorcardiography and realistic potentials. In Computing in Cardiology, 2013.
We are developing a new technique to reconstruct electrical heart activity by exploiting characteristics of so-called ‘wavelets’. The idea is that by representing the epicardial potentials by wavelets, we can take advantage of sparsity and achieve results that are less influenced by noise. Find the corresponding conference paper here and the poster below.
Reference: Matthijs Cluitmans, Joel Karel, Pietro Bonizzi, Paul Volders, Ronald Westra, and Ralf Peeters. Wavelet-sparsity based regularization over time in the inverse problem of electrocardiography. In Engineering in Medicine and Biology Society (EMBC), 2013 Annual International Conference of the IEEE, 2013 in press.
One of the major issues in the inverse problem of electrocardiography is the sensitivity to noise (or in mathematically more correct terms, the ill-posedness of the problem). In general, research teams add constraints to the possible solutions to reduce the sensitivity to noise. These constrains are mainly based on physical or mathematical characteristics of the inverse problem. But what if we could use constraints that are actually based on more “biological” knowledge of the heart? In this conference paper we have seen some encouraging results when we try to achieve this by using simulated electrical training data on a heart surface.
Reference: Matthijs Cluitmans, Ralf Peeters, Paul Volders, and Ronald Westra. Realistic training data improve noninvasive reconstruction of heart-surface potentials. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pages 6373-6376. IEEE, 2012.