Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain

A primary factor for the success of machine learning is the quality of labeled training data. However, in many fields, labeled data can be costly, difficult, or even impossible to acquire. In comparison, computer simulation data can now be generated at a much higher abundance with a much lower cost. These simulation data could potentially solve the problem of data deficiency in many machine learning tasks. Nevertheless, due to model assumptions, simplifications and possible errors, there is always a discrepancy between simulated and real data. This discrepancy needs to be addressed when transferring the knowledge from simulation to real data. Furthermore, simulation data is always tied to specific settings of models parameters, many of which have a considerable range of variations yet not necessarily relevant to the machine learning task of interest. The knowledge extracted from simulation data must thus be generalizable across these parameter variations before being transferred. In this dissertation, we address the two outlined challenges in leveraging simulation data to overcome the shortage of labeled real data, . We do so in a clinical task of localizing the origin of ventricular activation from 12 lead electrocardiograms (ECGs), where the clinical ECG data with labeled sites of origin in the heart can only be invasively available. By adopting the concept of domain adaptation, we address the discrepancy between simulated and clinical ECG data by learning the shift between the two domains using a large amount of simulation data and a small amount of clinical data. By adopting the concept of domain generalization, we then address the reliance of simulated ECG data on patient-specific geometrical models by learning to generalize simulated ECG data across subjects, before transferring them to clinical data. Evaluated on in-vivo premature ventricular contraction (PVC) patients, we demonstrate the feasibility of utilizing a large number of offline simulated ECG datasets to enable the prediction of the origin of arrhythmia with only a small number of clinical ECG data on a new patient

Electrocardiogram Signal Analysis and Simulations for Non-Invasive Diagnosis - Model-Based and Data-Driven Approaches for the Estimation of Ionic Concentrations and Localization of Excitation Origins

Das Elektrokardiogramm (EKG) ist die Standardtechnik zur Messung der elektrischen Aktivität des Herzens. EKG-Geräte sind verfügbar, kostengünstig und erlauben zudem eine nichtinvasive Messung. Das ist insbesondere wichtig für die Diagnose von kardiovaskulären Erkrankungen (KVE). Letztere sind mit verursachten Kosten von 210 Milliarden Euro eine der Hauptbelastungen für das Gesundheitssystem in Europa und dort der Grund für 3,9 Millionen Todesfälle – dies entspricht 45% aller Todesfälle. Neben weiteren Risikofaktoren spielen chronische Nierenerkrankungen und strukturelle Veränderungen des Herzgewebes eine entscheidende Rolle für das Auftreten von KVE. Deshalb werden in dieser Arbeit zwei Pathologien, die in Verbindung zu KVE stehen, betrachtet: Elektrolytkonzentrationsveränderungen bei chronisch Nierenkranken und ektope Foki, die autonom Erregungen iniitieren. In beiden Projekten ist die Entwicklung von Methoden mithilfe von simulierten Signalen zur Diagnoseunterstützung das übergeordnete Ziel. Im ersten Projekt helfen simulierte EKGs die Signalverarbeitungskette zur EKG-basierten Schätzung der Ionenkonzentrationen von Kalium und Calcium zu optimieren. Die Erkenntnisse dieser Optimierung fließen in zwei patienten-spezifische Methoden zur Kaliumkonzentrationsschätzung ein, die wiederum mithilfe von Patientendaten ausgewertet werden. Die Methoden lieferten im Mittel einen absoluten Fehler von 0,37 mmol/l für einen patienten-spezifischen Ansatz und 0,48 mmol/l für einen globalen Ansatz mit zusätzlicher patienten-spezifischer Korrektur. Die Vorteile der Schätzmethoden werden gegenüber bereits existierender Ansätze dargelegt. Alle entwickelten Algorithmen sind ferner unter einer Open-Source-Lizenz veröffentlicht. Das zweite Projekt zielte auf die Lokalisierung von ektopen Foki mithilfe des EKGs ohne die Nutzung der individuellen Patientengeometrie. 1.766.406 simulierte EKG-Signale (Body Surface Potential Maps (BSPMs)) wurden zum Trainieren von zwei Convolutional Neural Networks (CNNs) erzeugt. Das erste CNN sorgt für die Schätzung von Anfang und Ende der Depolarisation der Ventrikel. Das zweite CNN nutzt die Information der Depolarisation im BSPM zur Schätzung des Erregungsurpsrungs. Der spezielle Aufbau des CNNs ermöglicht die Darstellung mehrerer Lösungen, wie sie durch Mehrdeutigkeiten im BSPM vorliegen können. Der kleinste Median des Lokalisierungsfehlers lag bei 1,54 mm für den Test-Datensatz der simulierten Signale, bzw. bei 37 mm für Patientensignale. Somit erlaubt die Kombination beider CNNs die verlässliche Lokalisierung von ektopen Foki auch anhand von Patientendaten, obwohl Patientendaten vorher nicht im Training genutzt wurden. Die Resultate dieser zwei Projekte demonstrieren, wie EKG-Simulationen zur Entwicklung und Verbesserung von EKG-Signalverarbeitungsmethoden eingesetzt werden und bei der Diagnosefindung helfen können. Zudem zeigt sich das Potential der Kombination von Simulationen und CNNs, um einerseits die zumeist raren klinischen Signale zu ersetzen und andererseits Modelle zu finden, die für mehrere Patienten/-innen gültig sind. Die vorgestellten Methoden bergen die Möglichkeit, die Diagnosestellungen zu beschleunigen und mit hoher Wahrscheinlichkeit den Therapieerfolg der Patienten zu verbessern

Personalized noninvasive imaging of volumetric cardiac electrophysiology

Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Non-invasive identification of atrial fibrillation drivers

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Nowadays the fibrillatory process is known to be provoked by the high-frequency reentrant activity of certain atrial regions that propagates the fibrillatory activity to the rest of the atrial tissue, and the electrical isolation of these key regions has demonstrated its effectiveness in terminating the fibrillatory process. The location of the dominant regions represents a major challenge in the diagnosis and treatment of this arrhythmia. With the aim to detect and locate the fibrillatory sources prior to surgical procedure, non-invasive methods have been developed such as body surface electrical mapping (BSPM) which allows to record with high spatial resolution the electrical activity on the torso surface or the electrocardiographic imaging (ECGI) which allows to non-invasively reconstruct the electrical activity in the atrial surface. Given the novelty of these systems, both technologies suffer from a lack of scientific knowledge about the physical and technical mechanisms that support their operation. Therefore, the aim of this thesis is to increase that knowledge, as well as studying the effectiveness of these technologies for the localization of dominant regions in patients with AF. First, it has been shown that BSPM systems are able to noninvasively identify atrial rotors by recognizing surface rotors after band-pass filtering. Furthermore, the position of such surface rotors is related to the atrial rotor location, allowing the distinction between left or right atrial rotors. Moreover, it has been found that the surface electrical maps in AF suffer a spatial smoothing effect by the torso conductor volume, so the surface electrical activity can be studied with a relatively small number of electrodes. Specifically, it has been seen that 12 uniformly distributed electrodes are sufficient for the correct identification of atrial dominant frequencies, while at least 32 leads are needed for non-invasive identification of atrial rotors. Secondly, the effect of narrowband filtering on the effectiveness of the location of reentrant patterns was studied. It has been found that this procedure allows isolating the reentrant electrical activity caused by the rotor, increasing the detection rate for both invasive and surface maps. However, the spatial smoothing caused by the regularization of the ECGI added to the temporal filtering causes a large increase in the spurious reentrant activity, making it difficult to detect real reentrant patterns. However, it has been found that maps provided by the ECGI without temporal filtering allow the correct detection of reentrant activity, so narrowband filtering should be applied for intracavitary or surface signal only. Finally, we studied the stability of the markers used to detect dominant regions in ECGI, such as frequency maps or the rotor presence. It has been found that in the presence of alterations in the conditions of the inverse problem, such as electrical or geometrical noise, these markers are significantly more stable than the ECGI signal morphology from which they are extracted. In addition, a new methodology for error reduction in the atrial spatial location based on the curvature of the curve L has been proposed. The results presented in this thesis showed that BSPM and ECGI systems allows to non-invasively locate the presence of high-frequency rotors, responsible for the maintenance of AF. This detection has been proven to be unambiguous and robust, and the physical and technical mechanisms that support this behavior have been studied. These results indicate that both non-invasive systems provide information of great clinical value in the treatment of AF, so their use can be helpful for selecting and planning atrial ablation procedures.La fibrilación auricular (FA) es una de las arritmias cardiacas más frecuentes. Hoy en día se sabe que el proceso fibrilatorio está provocado por la actividad reentrante a alta frecuencia de ciertas regiones auriculares que propagan la actividad fibrilatoria en el resto del tejido auricular, y se ha demostrado que el aislamiento eléctrico de estas regiones dominantes permite detener el proceso fibrilatorio. La localización de las regiones dominantes supone un gran reto en el diagnóstico y tratamiento de la FA. Con el objetivo de poder localizar las fuentes fibrilatorias con anterioridad al procedimiento quirúrgico, se han desarrollado métodos no invasivos como la cartografía eléctrica de superficie (CES) que registra con gran resolución espacial la actividad eléctrica en la superficie del torso o la electrocardiografía por imagen (ECGI) que permite reconstruir la actividad eléctrica en la superficie auricular. Dada la novedad de estos sistemas, existe una falta de conocimiento científico sobre los mecanismos físicos y técnicos que sustentan su funcionamiento. Por lo tanto, el objetivo de esta tesis es aumentar dicho conocimiento, así como estudiar la eficacia de ambas tecnologías para la localización de regiones dominantes en pacientes con FA. En primer lugar, ha visto que los sistemas CES permiten identificar rotores auriculares mediante el reconocimiento de rotores superficiales tras el filtrado en banda estrecha. Además, la posición de los rotores superficiales está relacionada con la localización de dichos rotores, permitiendo la distinción entre rotores de aurícula derecha o izquierda. Por otra parte, se ha visto que los mapas eléctricos superficiales durante FA sufren una gran suavizado espacial por el efecto del volumen conductor del torso, lo que permite que la actividad eléctrica superficial pueda ser estudiada con un número relativamente reducido de electrodos. Concretamente, se ha visto que 12 electrodos uniformemente distribuidos son suficientes para una correcta identificación de frecuencias dominantes, mientras que son necesarios al menos 32 para una correcta identificación de rotores auriculares. Por otra parte, también se ha estudiado el efecto del filtrado en banda estrecha sobre la eficacia de la localización de patrones reentrantes. Así, se ha visto que este procedimiento permite aislar la actividad eléctrica reentrante provocada por el rotor, aumentando la tasa de detección tanto para señal obtenida de manera invasiva como para los mapas superficiales. No obstante, este filtrado temporal sobre la señal de ECGI provoca un gran aumento de la actividad reentrante espúrea que dificulta la detección de patrones reentrantes reales. Sin embargo, los mapas ECGI sin filtrado temporal permiten la detección correcta de la actividad reentrante, por lo el filtrado debería ser aplicado únicamente para señal intracavitaria o superficial. Por último, se ha estudiado la estabilidad de los marcadores utilizados en ECGI para detectar regiones dominantes, como son los mapas de frecuencia o la presencia de rotores. Se ha visto que en presencia de alteraciones en las condiciones del problema inverso, como ruido eléctrico o geométrico, estos marcadores son significativamente más estables que la morfología de la propia señal ECGI. Además, se ha propuesto una nueva metodología para la reducción del error en la localización espacial de la aurícula basado en la curvatura de la curva L. Los resultados presentados en esta tesis revelan que los sistemas de CES y ECGI permiten localizar de manera no invasiva la presencia de rotores de alta frecuencia. Esta detección es univoca y robusta, y se han estudiado los mecanismos físicos y técnicos que sustentan dicho comportamiento. Estos resultados indican que ambos sistemas no invasivos proporcionan información de gran valor clínico en el tratamiento de la FA, por lo que su uso puede ser de gran ayuda para la selección y planificaciLa fibril·lació auricular (FA) és una de les arítmies cardíaques més freqüents. Hui en dia es sabut que el procés fibrilatori està provocat per l'activitat reentrant de certes regions auriculars que propaguen l'activitat fibril·latoria a la resta del teixit auricular, i s'ha demostrat que l'aïllament elèctric d'aquestes regions dominants permet aturar el procés fibrilatori. La localització de les regions dominants suposa un gran repte en el diagnòstic i tractament d'aquesta arítmia. Amb l'objectiu de poder localitzar fonts fibril·latories amb anterioritat al procediment quirúrgic s'han desenvolupat mètodes no invasius com la cartografia elèctrica de superfície (CES) que registra amb gran resolució espacial l'activitat elèctrica en la superfície del tors o l'electrocardiografia per imatge (ECGI) que permet obtenir de manera no invasiva l'activitat elèctrica en la superfície auricular. Donada la relativa novetat d'aquests sistemes, existeix una manca de coneixement científic sobre els mecanismes físics i tècnics que sustenten el seu funcionament. Per tant, l'objectiu d'aquesta tesi és augmentar aquest coneixement, així com estudiar l'eficàcia d'aquestes tecnologies per a la localització de regions dominants en pacients amb FA. En primer lloc, s'ha vist que els sistemes CES permeten identificar rotors auriculars mitjançant el reconeixement de rotors superficials després del filtrat en banda estreta. A més, la posició dels rotors superficials està relacionada amb la localització d'aquests rotors, permetent la distinció entre rotors de aurícula dreta o esquerra. També s'ha vist que els mapes elèctrics superficials durant FA pateixen un gran suavitzat espacial per l'efecte del volum conductor del tors, el que permet que l'activitat elèctrica superficial pugui ser estudiada amb un nombre relativament reduït d'elèctrodes. Concretament, s'ha vist que 12 elèctrodes uniformement distribuïts són suficients per a una correcta identificació de freqüències dominants auriculars, mentre que són necessaris almenys 32 per a una correcta identificació de rotors auriculars. D'altra banda, també s'ha estudiat l'efecte del filtrat en banda estreta sobre l'eficàcia de la localització de patrons reentrants. Així, s'ha vist que aquest procediment permet aïllar l'activitat elèctrica reentrant provocada pel rotor, augmentant la taxa de detecció tant pel senyal obtingut de manera invasiva com per als mapes superficials. No obstant això, aquest filtrat temporal sobre el senyal de ECGI provoca un gran augment de l'activitat reentrant espúria que dificulta la detecció de patrons reentrants reals. A més, els mapes proporcionats per la ECGI sense filtrat temporal permeten la detecció correcta de l'activitat reentrant, per la qual cosa el filtrat hauria de ser aplicat únicament per a senyal intracavitària o superficial. Per últim, s'ha estudiat l'estabilitat dels marcadors utilitzats en ECGI per a detectar regions auriculars dominants, com són els mapes de freqüència o la presència de rotors. S'ha vist que en presència d'alteracions en les condicions del problema invers, com soroll elèctric o geomètric, aquests marcadors són significativament més estables que la morfologia del mateix senyal ECGI. A més, s'ha proposat una nova metodologia per a la reducció de l'error en la localització espacial de l'aurícula basat en la curvatura de la corba L. Els resultats presentats en aquesta tesi revelen que els sistemes de CES i ECGI permeten localitzar de manera no invasiva la presència de rotors d'alta freqüència. Aquesta detecció és unívoca i robusta, i s'han estudiat els mecanismes físics i tècnics que sustenten aquest comportament. Aquests resultats indiquen que els dos sistemes no invasius proporcionen informació de gran valor clínic en el tractament de la FA, pel que el seu ús pot ser de gran ajuda per a la selecció i planificació de procediments d'ablació auricular.Rodrigo Bort, M. (2016). Non-invasive identification of atrial fibrillation drivers [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/75346TESISPremios Extraordinarios de tesis doctorale

Doctor of Philosophy

dissertationInverse Electrocardiography (ECG) aims to noninvasively estimate the electrophysiological activity of the heart from the voltages measured at the body surface, with promising clinical applications in diagnosis and therapy. The main challenge of this emerging technique lies in its mathematical foundation: an inverse source problem governed by partial differential equations (PDEs) which is severely ill-conditioned. Essential to the success of inverse ECG are computational methods that reliably achieve accurate inverse solutions while harnessing the ever-growing complexity and realism of the bioelectric simulation. This dissertation focuses on the formulation, optimization, and solution of the inverse ECG problem based on finite element methods, consisting of two research thrusts. The first thrust explores the optimal finite element discretization specifically oriented towards the inverse ECG problem. In contrast, most existing discretization strategies are designed for forward problems and may become inappropriate for the corresponding inverse problems. Based on a Fourier analysis of how discretization relates to ill-conditioning, this work proposes refinement strategies that optimize approximation accuracy o f the inverse ECG problem while mitigating its ill-conditioning. To fulfill these strategies, two refinement techniques are developed: one uses hybrid-shaped finite elements whereas the other adapts high-order finite elements. The second research thrust involves a new methodology for inverse ECG solutions called PDE-constrained optimization, an optimization framework that flexibly allows convex objectives and various physically-based constraints. This work features three contributions: (1) fulfilling optimization in the continuous space, (2) formulating rigorous finite element solutions, and (3) fulfilling subsequent numerical optimization by a primal-dual interiorpoint method tailored to the given optimization problem's specific algebraic structure. The efficacy o f this new method is shown by its application to localization o f cardiac ischemic disease, in which the method, under realistic settings, achieves promising solutions to a previously intractable inverse ECG problem involving the bidomain heart model. In summary, this dissertation advances the computational research of inverse ECG, making it evolve toward an image-based, patient-specific modality for biomedical research

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

Solving the inverse problem of electrocardiography in a realistic environment

Heart disease is a leading cause of death worldwide. Straightforward information about the cardiac electrophysiology can help to improve the quality of diagnosis of heart diseases. The inverse problem of electrocardiography and the intracardiac catheter measurement are two ways to get access to the electrophysiology in the heart. In this thesis six research topics related to these two techniques are included

Design and clinical validation of novel imaging strategies for analysis of arrhythmogenic substrate

_CURRENT CHALLENGES IN ELECTROPHYSIOLOGY_ Technical advances in cardiovascular electrophysiology have resulted in an increasing number of catheter ablation procedures reaching 200 000 in Europe for the year 2013. These advanced interventions are often complex and time consuming and may cause significant radiation exposure. Furthermore, a substantial number of ablation procedures remain associated with poor (initial) outcomes and frequently require ≥1 redo procedures. Innovations in modalities for substrate imaging could facilitate our understanding of the arrhythmogenic substrate, improve the design of patient-specific ablation strategies and improve the results of ablation procedures. _NOVEL SUBSTRATE IMAGING MODALITIES_ __Cardiac magnetic resonance__ Cardiac magnetic resonance imaging (CMR) can be considered the most comprehensive and suitable modality for the complete electrophysiology and catheter ablation workup (including patient selection, procedural guidance, and [procedural] follow-up). Utilizing inversion recovery CMR, fibrotic myocardium can be visualized and quantified 10–15 min after intravenous administration of Gadolinium contrast. This imaging technique is known as late Gadolinium enhancement (LGE) imaging. Experimental models have shown excellent agreement between size and shape in LGE CMR and areas of myocardial infarction by histopathology. Recent studies have also demonstrated how scar size, shape and location from pre-procedural LGE can be useful in guiding ventricular tachycardia’s (VT) ablation or atrial fibrillation (AF) ablation. These procedures are often time-consuming due to the preceding electrophysiological mapping study required to identify slow conduction zones involved in re-entry circuits. Post-processed LGE images provide scar maps, which could be integrated with electroanatomic mapping systems to facilitate these procedures. __Inverse potential mapping__ Through the years, various noninvasive electrocardiographic imaging techniques have emerged that estimate epicardial potentials or myocardial activation times from potentials recorded on the thorax. Utilizing an inverse procedure, the potentials on the heart surface or activation times of the myocardium are estimated with the recorded body surface potentials as source data. Although this procedure only estimates the time course of unipolar epicardial electrograms, several studies have demonstrated that the epicardial potentials and electrograms provide substantial information about intramyocardial activity and have great potential to facilitate risk-stratification and generate personalized ablation strategies. __Objectives of this thesis__ 1. To evaluate the utility of cardiac magnetic resonance derived geometrical and tissue characteristic information for patient stratification and guidance of AF ablation. 2. To design and evaluate the performance of a finite element model based inverse potential mapping in predicting the arrhythmogenic focus in idiopathic ventricular tachycardia using invasive electro-anatomical activation mapping as a reference standard

Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies

[ES] Las arritmias cardíacas son un problema importante para los sistemas de salud en el mundo desarrollado debido a su alta incidencia y prevalencia a medida que la población envejece. La fibrilación auricular (FA) y la fibrilación ventricular (FV) se encuentran entre las arritmias más complejas observadas en la práctica clínica. Las consecuencias clínicas de tales alteraciones arrítmicas incluyen el desarrollo de eventos cardioembólicos complejos en la FA, y repercusiones dramáticas debido a procesos fibrilatorios sostenidos que amenazan la vida infringiendo daño neurológico tras paro cardíaco por FV, y que pueden provocar la muerte súbita cardíaca (MSC). Sin embargo, a pesar de los avances tecnológicos de las últimas décadas, sus mecanismos intrínsecos se comprenden de forma incompleta y, hasta la fecha, las estrategias terapéuticas carecen de una base mecanicista suficiente y poseen bajas tasas de éxito. Entre los mecanismos implicados en la inducción y perpetuación de arritmias cardíacas, como la FA, se cree que las dinámicas de las fuentes focales y reentrantes de alta frecuencia, en sus diferentes modalidades, son las fuentes primarias que mantienen la arritmia. Sin embargo, se sabe poco sobre los atractores, así como, de la dinámica espacio-temporal de tales fuentes fibrilatorias primarias, específicamente, las fuentes focales o rotacionales dominantes que mantienen la arritmia. Por ello, se ha desarrollado una plataforma computacional, para comprender los factores (activos, pasivos y estructurales) determinantes, y moduladores de dicha dinámica. Esto ha permitido establecer un marco para comprender la compleja dinámica de los rotores con énfasis en sus propiedades deterministas para desarrollar herramientas basadas en los mecanismos para ayuda diagnóstica y terapéutica. Comprender los procesos fibrilatorios es clave para desarrollar marcadores y herramientas fisiológica- y clínicamente relevantes para la ayuda de diagnóstico temprano. Específicamente, las propiedades espectrales y de tiempo-frecuencia de los procesos fibrilatorios han demostrado resaltar el comportamiento determinista principal de los mecanismos intrínsecos subyacentes a las arritmias y el impacto de tales eventos arrítmicos. Esto es especialmente relevante para determinar el pronóstico temprano de los supervivientes comatosos después de un paro cardíaco debido a fibrilación ventricular (FV). Las técnicas de mapeo electrofisiológico, el mapeo eléctrico y óptico cardíaco, han demostrado ser recursos muy valiosos para dar forma a nuevas hipótesis y desarrollar nuevos enfoques mecanicistas y estrategias terapéuticas mejoradas. Esta tecnología permite además el trabajo multidisciplinar entre clínicos y bioingenieros, para el desarrollo y validación de dispositivos y metodologías para identificar biomarcadores multi-dominio que permitan rastrear con precisión la dinámica de las arritmias identificando fuentes dominantes y atractores con alta precisión para ser dianas de estrategias terapeúticas innovadoras. Es por ello que uno de los objetivos fundamentales ha sido la implantación y validación de nuevos sistemas de mapeo en distintas configuraciones que sirvan de plataforma de desarrollo de nuevas estrategias terapeúticas. Aunque el mapeo panorámico es el método principal y más completo para rastrear simultáneamente biomarcadores electrofisiológicos, su adopción por la comunidad científica es limitada principalmente debido al coste elevado de la tecnología. Aprovechando los avances tecnológicos recientes, nos hemos enfocado en desarrollar, y validar, sistemas de mapeo óptico de alta resolución para registro panorámico cardíaco, utilizando modelos clínicamente relevantes para la investigación básica y la bioingeniería.[CA] Les arítmies cardíaques són un problema important per als sistemes de salut del món desenvolupat a causa de la seva alta incidència i prevalença a mesura que la població envelleix. La fibril·lació auricular (FA) i la fibril·lació ventricular (FV), es troben entre les arítmies més complexes observades a la pràctica clínica. Les conseqüències clíniques d'aquests trastorns arítmics inclouen el desenvolupament d'esdeveniments cardioembòlics complexos en FA i repercussions dramàtiques a causa de processos fibril·latoris sostinguts que posen en perill la vida amb danys neurològics posteriors a la FV, que condueixen a una aturada cardíaca i a la mort cardíaca sobtada (SCD). Tanmateix, malgrat els avanços tecnològics de les darreres dècades, els seus mecanismes intrínsecs s'entenen de forma incompleta i, fins a la data, les estratègies terapèutiques no tenen una base mecanicista suficient i tenen baixes taxes d'èxit. La majoria dels avenços en el desenvolupament de biomarcadors òptims i noves estratègies terapèutiques en aquest camp provenen de tècniques valuoses en la investigació de mecanismes d'arítmia. Entre els mecanismes implicats en la inducció i perpetuació de les arítmies cardíaques, es creu que les fonts primàries subjacents a l'arítmia són les fonts focals reingressants d'alta freqüència dinàmica i AF, en les seves diferents modalitats. Tot i això, se sap poc sobre els atractors i la dinàmica espaciotemporal d'aquestes fonts primàries fibril·ladores, específicament les fonts rotacionals o focals dominants que mantenen l'arítmia. Per tant, s'ha desenvolupat una plataforma computacional per entendre determinants actius, passius, estructurals i moduladors d'aquestes dinàmiques. Això va permetre establir un marc per entendre la complexa dinàmica multidomini dels rotors amb ènfasi en les seves propietats deterministes per desenvolupar enfocaments mecanicistes per a l'ajuda i la teràpia diagnòstiques. La comprensió dels processos fibril·latoris és clau per desenvolupar puntuacions i eines rellevants fisiològicament i clínicament per ajudar al diagnòstic precoç. Concretament, les propietats espectrals i de temps-freqüència dels processos fibril·latoris han demostrat destacar un comportament determinista important dels mecanismes intrínsecs subjacents a les arítmies i l'impacte d'aquests esdeveniments arítmics. Mitjançant coneixements previs, processament de senyals, tècniques d'aprenentatge automàtic i anàlisi de dades, es va desenvolupar una puntuació de risc mecanicista a la aturada cardíaca per FV. Les tècniques de cartografia òptica cardíaca i electrofisiològica han demostrat ser recursos inestimables per donar forma a noves hipòtesis i desenvolupar nous enfocaments mecanicistes i estratègies terapèutiques. Aquesta tecnologia ha permès durant molts anys provar noves estratègies terapèutiques farmacològiques o ablatives i desenvolupar mètodes multidominis per fer un seguiment precís de la dinàmica d'arrímies que identifica fonts i atractors dominants. Tot i que el mapatge panoràmic és el mètode principal per al seguiment simultani de paràmetres electrofisiològics, la seva adopció per part de la comunitat multidisciplinària d'investigació cardiovascular està limitada principalment pel cost de la tecnologia. Aprofitant els avenços tecnològics recents, ens centrem en el desenvolupament i la validació de sistemes de mapes òptics de baix cost per a imatges panoràmiques mitjançant models clínicament rellevants per a la investigació bàsica i la bioenginyeria.[EN] Cardiac arrhythmias are a major problem for health systems in the developed world due to their high incidence and prevalence as the population ages. Atrial fibrillation (AF) and ventricular fibrillation (VF), are amongst the most complex arrhythmias seen in the clinical practice. Clinical consequences of such arrhythmic disturbances include developing complex cardio-embolic events in AF, and dramatic repercussions due to sustained life-threatening fibrillatory processes with subsequent neurological damage under VF, leading to cardiac arrest and sudden cardiac death (SCD). However, despite the technological advances in the last decades, their intrinsic mechanisms are incompletely understood, and, to date, therapeutic strategies lack of sufficient mechanistic basis and have low success rates. Most of the progress for developing optimal biomarkers and novel therapeutic strategies in this field has come from valuable techniques in the research of arrhythmia mechanisms. Amongst the mechanisms involved in the induction and perpetuation of cardiac arrhythmias such AF, dynamic high-frequency re-entrant and focal sources, in its different modalities, are thought to be the primary sources underlying the arrhythmia. However, little is known about the attractors and spatiotemporal dynamics of such fibrillatory primary sources, specifically dominant rotational or focal sources maintaining the arrhythmia. Therefore, a computational platform for understanding active, passive and structural determinants, and modulators of such dynamics was developed. This allowed stablishing a framework for understanding the complex multidomain dynamics of rotors with enphasis in their deterministic properties to develop mechanistic approaches for diagnostic aid and therapy. Understanding fibrillatory processes is key to develop physiologically and clinically relevant scores and tools for early diagnostic aid. Specifically, spectral and time-frequency properties of fibrillatory processes have shown to highlight major deterministic behaviour of intrinsic mechanisms underlying the arrhythmias and the impact of such arrhythmic events. Using prior knowledge, signal processing, machine learning techniques and data analytics, we aimed at developing a reliable mechanistic risk-score for comatose survivors of cardiac arrest due to VF. Cardiac optical mapping and electrophysiological mapping techniques have shown to be unvaluable resources to shape new hypotheses and develop novel mechanistic approaches and therapeutic strategies. This technology has allowed for many years testing new pharmacological or ablative therapeutic strategies, and developing multidomain methods to accurately track arrhymia dynamics identigying dominant sources and attractors. Even though, panoramic mapping is the primary method for simultaneously tracking electrophysiological parameters, its adoption by the multidisciplinary cardiovascular research community is limited mainly due to the cost of the technology. Taking advantage of recent technological advances, we focus on developing and validating low-cost optical mapping systems for panoramic imaging using clinically relevant models for basic research and bioengineering.Calvo Saiz, CJ. (2022). Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182329TESI