In our recent paper we start to unravel the mechanisms of “unexplained” sudden cardiac arrest.
Looking back, we often understand what happened when somebody had sudden cardiac arrest (SCA). Sometimes, we don’t know what happened, even after extensive diagnostics. Especially in young, apparently healthy individuals, unexplained SCA has a major impact, despite being rare. We employed electrocardiographic imaging (ECGI) in survivors of unexplained SCA to get more insight in the arrhythmia substrate. ECGI extends the clinical electrocardiogram by using many more electrodes and imaging. We used it to study electrical recovery that happens after each heartbeat. In survivors of SCA in Maastricht UMC+, we found (clinically concealed) abnormalities in the electrical recovery of their hearts. In particular, these SCA survivors often have regions of early recovery next to regions of late recovery, with steep recovery gradients in between, and premature beats originating from the early recovery region. In experiments with explanted hearts at IHU LIRYC and in computer models (with support of Philips) we show that these premature beats may interact with the recovery gradients and lead to life-threatening arrhythmias. A tl/dr summary can be found in this Twitter thread.
These findings may help to provide targets for early diagnosis and improved therapy for sudden cardiac arrest, which we will need to study with future work.
Objective The purpose of this study was to evaluate the accuracy of noninvasive reconstructions of epicardial potentials, electrograms, activation and recovery isochrones, and beat origins by simultaneously performing electrocardiographic imaging (ECGI) and invasive epicardial electrography in intact animals.
Background Noninvasive imaging of electrical potentials at the epicardium, known as ECGI, is increasingly applied in patients to assess normal and abnormal cardiac electrical activity.
Methods Body-surface potentials and epicardial potentials were recorded in normal anesthetized dogs. Computed tomography scanning provided a torso-heart geometry that was used to reconstruct epicardial potentials from body-surface potentials.
Results Electrogram reconstructions attained a moderate accuracy compared with epicardial recordings (median correlation coefficient: 0.71), but with considerable variation (interquartile range: 0.36 to 0.86). This variation could be explained by a spatial mismatch (overall resolution was <20 mm) that was most apparent in regions with electrographic transition. More accurate derivation of activation times (Pearson R: 0.82), recovery times (R: 0.73), and the origin of paced beats (median error: 10 mm; interquartile range: 7 to 17 mm) was achieved by a spatiotemporal approach that incorporates the characteristics of the respective electrogram and neighboring electrograms. Reconstruction of beats from repeated single-site pacing showed a stable localization of origin. Cardiac motion, currently ignored in ECGI, correlates negatively with reconstruction accuracy.
Conclusions ECGI shows a decent median accuracy, but variability in electrogram reconstruction can be sizable. At present, therefore, clinical interpretations of ECGI should not be made on the basis of single electrograms only. Incorporating local spatiotemporal characteristics allows for accurate reconstruction of epicardial activation and recovery patterns, and beat origin localization to a 10-mm precision. Even more reliable interpretations are expected when the influences of cardiac motion are accounted for in ECGI.
Reference: Matthijs J.M.Cluitmans, PietroBonizzi, Joël M.H.Karel, MarcoDas, Bas L.J.H.Kietselaer, Monique M.J.de Jong, Frits W.Prinzen, Ralf L.M.Peeters, Ronald L.Westra, Paul G.A.Volders. In Vivo Validation of Electrocardiographic Imaging. JACC: Clinical Electrophysiology Feb 2017, 319; DOI: 10.1016/j.jacep.2016.11.012
The inverse problem of electrocardiography aims at noninvasively reconstructing electrical activity of the heart from recorded body-surface electrocardiograms. A crucial step is regularization, which deals with ill-posedness of the problem by imposing constraints on the possible solutions. We developed a regularization method that includes electrophysiological input. Body-surface potentials are recorded and a computed tomography scan is performed to obtain the torso–heart geometry. Propagating waveforms originating from several positions at the heart are simulated and used to generate a set of basis vectors representing spatial distributions of potentials on the heart surface. The real heart-surface potentials are then reconstructed from the recorded body-surface potentials by finding a sparse representation in terms of this basis. This method, which we named ‘physiology-based regularization’ (PBR), was compared to traditional Tikhonov regularization and validated using in vivo recordings in dogs. PBR recovered details of heart-surface electrograms that were lost with traditional regularization, attained higher correlation coefficients and led to improved estimation of recovery times. The best results were obtained by including approximate knowledge about the beat origin in the PBR basis.
Reference: Matthijs Cluitmans, Michael Clerx, Nele Vandersickel, Ralf Peeters, Paul Volders and Ronald Westra. Physiology-based regularization of the electrocardiographic inverse problem. In Medical & Biological Engineering & Computing, Nov 2016. Pubmed
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.
Recently, we have published a review about noninvasive reconstruction of cardiac electrical activity. In this review, we aim at providing both an overview of the technical background and clinical application of a broad range of noninvasive inverse imaging techniques.
Cluitmans MJ, Peeters RL, Westra RL, Volders PG. Noninvasive reconstruction of cardiac electrical activity: update on current methods, applications and challenges. Neth Heart J. 2015 Apr 21. [Epub ahead of print]
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.
We are selected winner in the Rosanna Degani Young Investigator Awards of the Computing in Cardiology Conference 2014, held in Boston, September 2014 with the following talk:
Physiology-based Regularization Improves Noninvasive Reconstruction and Localization of Cardiac Electrical Activity Matthijs JM Cluitmans, Monique MJ de Jong, Paul GA Volders, Ralf LM Peeters and Ronald L Westra
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.