Non-Invasive Electrocardiographic Imaging of Ventricular Activities: Data-Driven and Model-Based Approaches

Die vorliegende Arbeit beleuchtet ausgewählte Aspekte der Vorwärtsmodellierung, so zum Beispiel die Simulation von Elektro- und Magnetokardiogrammen im Falle einer elektrisch stillen Ischämie sowie die Anpassung der elektrischen Potentiale unter Variation der Leitfähigkeiten. Besonderer Fokus liegt auf der Entwicklung neuer Regularisierungsalgorithmen sowie der Anwendung und Bewertung aktuell verwendeter Methoden in realistischen in silico bzw. klinischen Studien

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.... (2018). Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications. Frontiers in Physiology. 9. https://doi.org/10.3389/fphys.2018.01305S9Andrews, C. M., Srinivasan, N. T., Rosmini, S., Bulluck, H., Orini, M., Jenkins, S., … Rudy, Y. (2017). Electrical and Structural Substrate of Arrhythmogenic Right Ventricular Cardiomyopathy Determined Using Noninvasive Electrocardiographic Imaging and Late Gadolinium Magnetic Resonance Imaging. Circulation: Arrhythmia and Electrophysiology, 10(7). doi:10.1161/circep.116.005105Aras, K., Good, W., Tate, J., Burton, B., Brooks, D., Coll-Font, J., … MacLeod, R. (2015). Experimental Data and Geometric Analysis Repository—EDGAR. Journal of Electrocardiology, 48(6), 975-981. doi:10.1016/j.jelectrocard.2015.08.008Austen, W., Edwards, J., Frye, R., Gensini, G., Gott, V., Griffith, L., … Roe, B. (1975). A reporting system on patients evaluated for coronary artery disease. 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On Learning and Generalization to Solve Inverse Problem of Electrophysiological Imaging

In this dissertation, we are interested in solving a linear inverse problem: inverse electrophysiological (EP) imaging, where our objective is to computationally reconstruct personalized cardiac electrical signals based on body surface electrocardiogram (ECG) signals. EP imaging has shown promise in the diagnosis and treatment planning of cardiac dysfunctions such as atrial flutter, atrial fibrillation, ischemia, infarction and ventricular arrhythmia. Towards this goal, we frame it as a problem of learning a function from the domain of measurements to signals. Depending upon the assumptions, we present two classes of solutions: 1) Bayesian inference in a probabilistic graphical model, 2) Learning from samples using deep networks. In both of these approaches, we emphasize on learning the inverse function with good generalization ability, which becomes a main theme of the dissertation. In a Bayesian framework, we argue that this translates to appropriately integrating different sources of knowledge into a common probabilistic graphical model framework and using it for patient specific signal estimation through Bayesian inference. In learning from samples setting, this translates to designing a deep network with good generalization ability, where good generalization refers to the ability to reconstruct inverse EP signals in a distribution of interest (which could very well be outside the sample distribution used during training). By drawing ideas from different areas like functional analysis (e.g. Fenchel duality), variational inference (e.g. Variational Bayes) and deep generative modeling (e.g. variational autoencoder), we show how we can incorporate different prior knowledge in a principled manner in a probabilistic graphical model framework to obtain a good inverse solution with generalization ability. Similarly, to improve generalization of deep networks learning from samples, we use ideas from information theory (e.g. information bottleneck), learning theory (e.g. analytical learning theory), adversarial training, complexity theory and functional analysis (e.g. RKHS). We test our algorithms on synthetic data and real data of the patients who had undergone through catheter ablation in clinics and show that our approach yields significant improvement over existing methods. Towards the end of the dissertation, we investigate general questions on generalization and stabilization of adversarial training of deep networks and try to understand the role of smoothness and function space complexity in answering those questions. We conclude by identifying limitations of the proposed methods, areas of further improvement and open questions that are specific to inverse electrophysiological imaging as well as broader, encompassing theory of learning and generalization

Impact of the Endocardium in a Parameter Optimization to Solve the Inverse Problem of Electrocardiography

Electrocardiographic imaging aims at reconstructing cardiac electrical events from electrical signals measured on the body surface. The most common approach relies on the inverse solution of the Laplace equation in the torso to reconstruct epicardial potential maps from body surface potential maps. Here we apply a method based on a parameter identification problem to reconstruct both activation and repolarization times. From an ansatz of action potential, based on the Mitchell-Schaeffer ionic model, we compute body surface potential signals. The inverse problem is reduced to the identification of the parameters of the Mitchell-Schaeffer model. We investigate whether solving the inverse problem with the endocardium improves the results or not. We solved the parameter identification problem on two different meshes: one with only the epicardium, and one with both the epicardium and the endocardium. We compared the results on both the heart (activation and repolarization times) and the torso. The comparison was done on validation data of sinus rhythm and ventricular pacing. We found similar results with both meshes in 6 cases out of 7: the presence of the endocardium slightly improved the activation times. This was the most visible on a sinus beat, leading to the conclusion that inclusion of the endocardium would be useful in situations where endo-epicardial gradients in activation or repolarization times play an important role

Non-Invasive Electrocardiographic Mapping of Arrhythmia and Arrhythmogenic substrate in the Human Ventricle.

PhD Theses.The ablation of ventricular tachycardia often involves mapping when the arrhythmia is ongoing. This is often limited by haemodynamic instability. Non-invasive electrocardiographic mapping (ECGI) may aid in the mapping process by allowing expedient localisation. However, insufficient testing of this technology against ground truth data has been conducted. Furthermore, the system could have utility in detection of arrhythmogenic substrate. Current clinical practice uses echocardiography to risk stratify patients for implantation of intracardiac defibrillators (ICDs). Invasive epicardial electrogram data was collected in 8 patients. Activation and repolarisation times were compared to ECGI derived data showing modest correlation. A detailed analysis of ventricular tachycardia sites of origin in the heart was elucidated using validated electrophysiological techniques. These were compared to ECGI derived data in 18 patients, showing better accuracy than the 12 lead ECG with a resolution of ~2.2cm suggesting it may be a useful adjunctive tool in mapping unstable VT. ECGI derived data collected during sinus rhythm was compared to invasive electrogram maps in 16 patients. The capacity of ECGI to localise scar showed modest accuracy. ECGI and Cardiac MRI scans were performed in 21 patients with cardiac amyloidosis. ECGI showed cardiac amyloidosis to be associated with both ventricular conduction and repolarization abnormalities, supporting the hypothesis that arrhythmic mechanisms may be linked to mortality in this condition

β1-adrenoceptor blockade treatment of right ventricular dysfunction caused by pulmonary hypertension

Failure of the right ventricle (or ventricular) (RV) is the leading cause of death in patients with pulmonary arterial hypertension (PAH), however no treatments specifically target the failing RV. β1-adrenoceptor blockers (β-blockers, BB) reduce mortality in left heart failure but current clinical guidelines caution against their use in PAH. Recent studies suggest β-blockers may be beneficial in PAH however the mechanisms remain unknown. The present study sought to establish whether the β1- blocker metoprolol (10 mg/kg/day) improved survival and function in a rat model of PAH induced by monocrotaline (60 mg/kg, MCT), and to elucidate the mechanisms responsible. Daily metoprolol or placebo was administered 15 days post-monocrotaline injection. PAH resulted in severe RV hypertrophy, dysfunction and heart failure by median day 23 in placebo treated rats (FAIL), whereas metoprolol extended the median survival to day 31 (MCT+BB). RV function measured by echocardiography and catheterisation was severely impaired in FAIL, but was partially restored in MCT+BB on day 23±1. Metoprolol appeared to act primarily on the myocardium and not the vasculature. Contractile abnormalities in isolated FAIL RV cardiomyocytes included increased cell volume, negative force and Ca2+ transient response to faster pacing, increased stiffness to stretch and shorter resting sarcomere length. Reduced creatine kinase activity was found in FAIL; creatine kinase inhibition reproduced characteristics of FAIL in healthy cells, whereas exogeneous creatine kinase reversed the shorter sarcomere length in FAIL cells. Contractile and Ca2+ handling properties of MCT+BB cells were partially or fully restored relative to healthy cells. Capillary density was reduced in FAIL and partially restored in MCT+BB; computer modelling indicated fewer areas of hypoxia in MCT+BB RV. Assessment of FAIL RV mitochondria revealed reduced creatine-coupled respiration but no other detectable defects. Metoprolol improved survival, Ca2+-handling, contractility, oxygen delivery and diastolic properties of PAH rats. β-blockers represent a novel myocardium-specific therapy to target the failing RV in PAH