Spatially Coherent Activation Maps for Electrocardiographic Imaging

International audienceObjective: Cardiac mapping is an important diagnostic step in cardiac electrophysiology. One of its purposes is to generate a map of the depolarization sequence. This map is constructed in clinical routine either by directly analyzing cardiac electrograms (EGM) recorded invasively or an estimate of these EGMs obtained by a non-invasive technique. Activation maps based on noninvasively estimated EGMs often show artefactual jumps in activation times. To overcome this problem we present a new method to construct the activation maps from reconstructed unipolar EGMs. Methods: On top of the standard estimation of local activation time from unipolar intrinsic deflections, we propose to mutually compare the EGMs in order to estimate the delays in activation for neighboring recording locations. We then describe a workflow to construct a spatially coherent activation map from local activation times and delay estimates in order to create more accurate maps. The method is optimized using simulated data and evaluated on clinical data from 12 different activation sequences. Results: We found that the standard methodology created lines of artificially strong activation time gradient. The proposed workflow enhanced these maps significantly. Conclusion: Estimating delays between neighbors is an interesting option for activation map computation in ECGi

Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications

[EN] Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.This study received financial support from the Hein Wellens Fonds, the Cardiovascular Research and Training Institute (CVRTI), the Nora Eccles Treadwell Foundation, the National Institute of General Medical Sciences of the National Institutes of Health (P41GM103545), the National Institutes of Health (NIH HL080093), the French government as part of the Investments of the Future program managed by the National Research Agency (ANR-10-IAHU-04), from the VEGA Grant Agency in Slovakia (2/0071/16), from the Slovak Research and Development Agency (APVV-14-0875), the Fondo Europeo de Desarrollo Regional (FEDER), the Instituto de Salud Carlos III (PI17/01106) and from Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana (AICO/2018/267) and NIH grant (HL125998) and National Science Foundation (ACI-1350374).Cluitmans, M.; Brooks, D.; Macleod, RS.; Dossel, O.; Guillem Sánchez, MS.; Van Dam, P.; Svehlikova, J.... 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The Impact of Torso Signal Processing on Noninvasive Electrocardiographic Imaging Reconstructions

Goal: To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data. Methods: Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded. Results: HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p < 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p < 0.05). Conclusion: BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur

Electrocardiographic Imaging Using a Spatio-Temporal Basis of Body Surface Potentials - Application to Atrial Ectopic Activity

Electrocardiographic imaging (ECGI) strongly relies on a priori assumptions and additional information to overcome ill-posedness. The major challenge of obtaining good reconstructions consists in finding ways to add information that effectively restricts the solution space without violating properties of the sought solution. In this work, we attempt to address this problem by constructing a spatio-temporal basis of body surface potentials (BSP) from simulations of many focal excitations. Measured BSPs are projected onto this basis and reconstructions are expressed as linear combinations of corresponding transmembrane voltage (TMV) basis vectors. The novel method was applied to simulations of 100 atrial ectopic foci with three different conduction velocities. Three signal-to-noise ratios (SNR) and bases of six different temporal lengths were considered. Reconstruction quality was evaluated using the spatial correlation coefficient of TMVs as well as estimated local activation times (LAT). The focus localization error was assessed by computing the geodesic distance between true and reconstructed foci. Compared with an optimally parameterized Tikhonov-Greensite method, the BSP basis reconstruction increased the mean TMV correlation by up to 22, 24, and 32% for an SNR of 40, 20, and 0 dB, respectively. Mean LAT correlation could be improved by up to 5, 7, and 19% for the three SNRs. For 0 dB, the average localization error could be halved from 15.8 to 7.9 mm. For the largest basis length, the localization error was always below 34 mm. In conclusion, the new method improved reconstructions of atrial ectopic activity especially for low SNRs. Localization of ectopic foci turned out to be more robust and more accurate. Preliminary experiments indicate that the basis generalizes to some extent from the training data and may even be applied for reconstruction of non-ectopic activity

Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps

Non-invasive localization of continuous atrial ectopic beats remains a cornerstone for the treatment of atrial arrhythmias. The lack of accurate tools to guide electrophysiologists leads to an increase in the recurrence rate of ablation procedures. Existing approaches are based on the analysis of the P-waves main characteristics and the forward body surface potential maps (BSPMs) or on the inverse estimation of the electric activity of the heart from those BSPMs. These methods have not provided an efficient and systematic tool to localize ectopic triggers. In this work, we propose the use of machine learning techniques to spatially cluster and classify ectopic atrial foci into clearly differentiated atrial regions by using the body surface P-wave integral map (BSPiM) as a biomarker. Our simulated results show that ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions and that new patterns could be correctly classified with an accuracy of 97% when considering 2 clusters and 96% for 4 clusters. Our results also suggest that an increase in the number of clusters is feasible at the cost of decreasing accuracy.This work was partially supported by The "Programa Prometeu" from Conselleria d'Educacio Formacio I Ocupacio, Generalitat Valenciana (www.edu.gva.es/fio/index_es.asp) Award Number: PROMETEU/2016/088 to JS; The "Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2013-2016" from the Ministerio de Economia, Industria y Competitividad of Spain, Agencia Estatal de Investigacion (www.mineco.gob.es) and the European Commission (European Regional Development Funds - ERDF -FEDER) (ec.europa.eu/regional_policy/es/funding/erdf/) Award Number: DPI2016-75799-R to JS and The "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientado a los Retos de la Sociedad" from the Ministerio de Economia y Competitividad of Spain, Agencia Estatal de Investigacion (www.mineco.gob.es) and the European Commission (European Regional Development Funds - ERDF -FEDER) (ec.europa.eu/regional_policy/es/funding/erdf/) Award Number: TIN2014-59932-JIN to AFA and RS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ferrer Albero, A.; Godoy, EJ.; Lozano, M.; Martínez Mateu, L.; Alonso Atienza, F.; Saiz Rodríguez, FJ.; Sebastián Aguilar, R. (2017). Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps. PLoS ONE. 12(7):1-23. https://doi.org/10.1371/journal.pone.0181263S12312

Noninvasive Electrocardiographic Imaging (ECGi) to Guide Catheter Ablation of Scar-related Ventricular Tachycardia

Scar-related VT is caused by local \textit{short circuits} of electrical propagation formed by slow-conducting channels of surviving tissue within a scar. Catheter ablation treats scar-related VT by destroying the critical channel of surviving tissues. Its efficacy heavily relies on how well the channels critical to the formation of VT circuits can be localized. Unfortunately, in current practice, this relies on invasive catheter mapping that falls short in several critical aspects: up to 90$\%$ of the VT circuits are too short-lived to be mapped, the mapping cannot be done non-invasively prior to the ablation procedure, and the mapping is restricted to one heart surface at a time. Electrocardiographic imaging (ECGi) is a noninvasive approach that reconstructs cardiac electrical signals from a very dense body surface electrocardiogram (ECG) in combination with patient-specific geometries of the heart and torso. In this dissertation, we investigate the clinical utility of ECGi in guiding catheter ablation of scar-related VT. Specifically, we investigate two open questions that are not well-understood in the potential of ECGi for mapping VT circuits. First, instead of commonly-used epicardial ECGi, we investigate the validity of simultaneous epicardial and endocardial ECGi mapping of VT circuits, and the possibility of using information from these two surfaces to infer the morphology of 3D circuits. Second, we investigate the integration of ECGi electrical information of VT circuits with magnetic resonance imaging (MRI) of scar analysis for joint electrical and structural delineation of the substrates for VT circuits. These studies were performed on a combination of computer simulation, animal model, and human subject data. Experimental results showed that epi-endo ECGi mapping could reconstruct VT circuits, differentiate 2D versus 3D circuits, and provide information about the location of the VT circuit beneath the surface. They also showed that integrated MRI-ECGi analysis offered a quantitative characterization of the scar substrate that forms a VT circuit. These outcomes showed that simultaneous epi-endo ECGi in the combination of MRI structural scar imaging may provide a viable augmentation to the current practice of invasive catheter mapping. It may help clinicians plan for the ablation prior to the procedure by equipping them with knowledge about a VT circuit\u27s critical components, the surfaces that are involved, and the 3D morphology of the VT circuit