Dynamical mechanism of atrial fibrillation: a topological approach

While spiral wave breakup has been implicated in the emergence of atrial fibrillation, its role in maintaining this complex type of cardiac arrhythmia is less clear. We used the Karma model of cardiac excitation to investigate the dynamical mechanisms that sustain atrial fibrillation once it has been established. The results of our numerical study show that spatiotemporally chaotic dynamics in this regime can be described as a dynamical equilibrium between topologically distinct types of transitions that increase or decrease the number of wavelets, in general agreement with the multiple wavelets hypothesis. Surprisingly, we found that the process of continuous excitation waves breaking up into discontinuous pieces plays no role whatsoever in maintaining spatiotemporal complexity. Instead this complexity is maintained as a dynamical balance between wave coalescence -- a unique, previously unidentified, topological process that increases the number of wavelets -- and wave collapse -- a different topological process that decreases their number.Comment: 15 pages, 14 figure

Spatial Characterization and Estimation of Intracardiac Propagation Patterns During Atrial Fibrillation

This doctoral thesis is in the field of biomedical signal processing with focus on methods for the analysis of atrial fibrillation (AF). Paper I of the present thesis addresses the challenge of extracting spatial properties of AF from body surface signals. Different parameters are extracted to estimate the preferred direction of atrial activation and the complexity of the atrial activation pattern. In addition, the relation of the spatial properties to AF organization, which is quantified by AF frequency, is evaluated. While no significant correlation between the preferred direction of atrial activation and AF frequency could be observed, the complexity of the atrial activation pattern was found to increase with AF frequency. The remaining three papers deal with the analysis of the propagation of the electrical activity in the atria during AF based on intracardiac signals. In Paper II, a time-domain method to quantify propagation patterns along a linear catheter based on the detected atrial activation times is developed. Taking aspects on intra-atrial signal organization into account, the detected activation times are combined into wavefronts, and parameters related to the consistency of the wavefronts over time and the activation order along the catheter are extracted. Furthermore, the potential relationship of the extracted parameters to established measures from body surface signals is investigated. While the degree of wavefront consistency was not reflected by the applied body surface measures, AF frequency could distinguish between recordings with different degrees of intra-atrial signal organization. This supports the role of AF frequency as an organization measure of AF. In Paper III, a novel method to analyze intracardiac propagation patterns based on causality analysis in the frequency domain is introduced. In particular, the approach is based on the partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The potential of the method is illustrated with simulation scenarios based on a detailed ionic model of the human atrial cell as well as with real data recordings, selected to present typical propagation mechanisms and recording situations in atrial tachyarrhythmias. For simulated data, the PDC is correctly reflecting the direction of coupling and thus the propagation between all recording sites. For real data, clear propagation patterns are identified which agree with previous clinical observations. Thus, the results illustrate the ability of the novel approach to identify propagation patterns from intracardiac signals during AF which can provide important information about the underlying AF mechanisms, potentially improving the planning and outcome of ablation. However, spurious couplings over long distances can be observed when analyzing real data comprised by a large number of simultaneously recorded signals, which gives room for further improvement of the method. The derivation of the PDC is entirely based on the fit of a multivariate autoregressive (MVAR) model, commonly estimated by the least-squares (LS) method. In Paper IV, the adaptive group least absolute selection and shrinkage operator (LASSO) is introduced in order to avoid overfitting of the MVAR model and to incorporate prior information such as sparsity of the solution. The sparsity can be motivated by the observation that direct couplings over longer distances are likely to be zero during AF; an information which has been further incorporated by proposing distance-adaptive group LASSO. In simulations, adaptive and distance-adaptive group LASSO are found to be superior to LS estimation in terms of both detection and estimation accuracy. In addition, the results of both simulations and real data analysis indicate that further improvements can be achieved when the distance between the recording sites is known or can be estimated. This further promotes the PDC as a method for analysis of AF propagation patterns, which may contribute to a better understanding of AF mechanisms as well as improved AF treatment

Endocardial activation mapping of human atrial fibrillation

Successful ablation of arrhythmias depends upon interpretation of the mechanism. However, in persistent atrial fibrillation (AF) ablation is currently directed towards the mechanism that initiates paroxysmal AF. We sought to address the hypothesis that atrial activation patterns during persistent AF may help determine the underlying mechanism. Activation mapping of AF wavefronts is labor intensive and often restricted to short time segments in limited atrial locations. RETRO-Mapping was developed to identify uniform wavefronts that occur during AF, and summate all wavefront vectors on to an orbital plot. Uniform wavefronts were mapped using RETRO-Mapping during sinus rhythm, atrial tachycardia, and atrial fibrillation, and validated against detailed manual analysis of the same wavefronts with conventional isochronal mapping. RETRO-Mapping was found to have comparable accuracy to isochronal mapping. RETRO-Mapping was then used to investigate atrial activation patterns during persistent AF. Atrial activation patterns demonstrated evidence of spatiotemporal stability over long time periods. Orbital plots created at different time points in the same location remained unchanged. Together with this important discovery, both fractionation and bipolar voltage were also demonstrated to express stability over time. Spatiotemporal stability during persistent AF enables sequential mapping as an acceptable technique. This property also allowed the development of a method for displaying sequentially mapped locations on a single map – RETRO-Choropleth Map. These findings go against the multiple wavelet hypothesis with random activation. Having gained insights in to these stable activation patterns, extensive analysis was undertaken to identify the presence of focal activation. Focal activations were identified during persistent AF. RETRO-Mapping was used to show that adjacent activation patterns were not related to focal activations. Lastly, the effect of pulmonary vein isolation (PVI) was studied by mapping atrial activation patterns before and after PVI. RETRO-Mapping showed that PVI leads to increased organisation of AF in most patients, supporting a mechanistic role of the pulmonary veins in persistent AF. In conclusion, a new technique has been developed and validated for automated activation mapping of persistent AF. These techniques could be used to guide additional ablation strategies beyond PVI for patients with persistent AF.Open Acces

The contact electrogram and its architectural determinants in atrial fibrillation

The electrogram is the sine qua non of excitable tissues, yet classification in atrial fibrillation (AF) remains poorly related to substrate factors. The objective of this thesis was to establish the relationship between electrograms and two commonly implicated substrate factors, connexin 43 and fibrosis in AF. The substrates and methods chosen to achieve this ranged from human acutely induced AF using open chest surgical mapping (Chapter 6), ex vivo whole heart Langendorff (Chapter 7) with in vivo telemetry confirming spontaneous AF in a new species of rat, the Brown Norway and finally isolated atrial preparations from an older cohort of rats using orthogonal pacing and novel co-localisation methods at sub-millimetre resolution and in some atria, optical mapping (Chapter 8). In rodents, electrode size and spacing was varied (Chapters 5, 10) to study its effects on structure function correlations (Chapter 9). Novel indices of AF organisation and automated electrogram morphology were used to quantify function (Chapter 4). Key results include the discoveries that humans without any history of prior AF have sinus rhythm electrograms with high spectral frequency content, that wavefront propagation velocities correlated with fibrosis and connexin phosphorylation ratios, that AF heterogeneity of conduction correlates to fibrosis and that orthogonal pacing in heavily fibrosed atria causes anisotropy in electrogram-fibrosis correlations. Furthermore, fibrosis and connexin 43 have differing and distinct spatial resolutions in their relationship with AF organisational indices. In conclusion a new model of AF has been found, and structure function correlations shown on an unprecedented scale, but with caveats of electrode size and direction dependence. These findings impact structure function methods and prove the effect of substrate on AF organisation.Open Acces

Patient-Specific Identification of Atrial Flutter Vulnerability–A Computational Approach to Reveal Latent Reentry Pathways

Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria. Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. In this way, all pathways along which AFlut can be sustained are identified. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut

High-Density Mapping Analysis of Electrical Spatiotemporal Behaviour in Atrial Fibrillation

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), 2022, Universidade de Lisboa, Faculdade de CiênciasDoenças cardiovasculares, tais como arritmias, são a principal causa de morte no mundo, especialmente no Sul e no Este da Ásia, e nos Estados Unidos da América [1]. As arritmas são caracterizadas pela alteração no ritmo sinusal normal do coração. Em particular, a fibrilhação auricular (FA) é a arritmia cardíaca mais comum na prática clínica, contribuindo para mais de 200 mil mortes globalmente em 2017 [2]. Caracteriza-se pela contração rápida e dessincronizada das aurículas, e está associada ao aumento da mortalidade e afecta de forma negativa a qualidade de vida dos pacientes. A FA é geralmente tratada através de medicação, porém quando esta falha, a ablação por cateter é indicada, sendo um tratamento de referência para combater esta patologia. A ablação apresenta uma taxa de sucesso de aproximadamente 50% no primeiro procedimento, sendo necessário efectuar vários procedimentos para aumentar a eficácia do tratamento [3]. A detecção desta patologia envolve, numa primeira fase, a realização de um electrocardiograma (ECG) e, posteriormente um estudo electrofisiológico para saber com precisão onde se localiza e o mecanismo subjacente à mesma. Este último implica o registo da actividade eléctrica através de electrogramas (EGM) locais em diferentes pontos das aurículas e dos ventrículos, com o auxílio de sistemas de mapeamento tridimensionais (3D) electroanatómicos, sendo um procedimento invasivo. Existem diversos métodos lineares e não lineares que permitem a análise dos EGMs nos domínios do tempo, frequência, fase, entre outros, com a finalidade de melhor compreender os mecanismos subjacentes à FA e, consequentemente aumentar a taxa de sucesso do processo de ablação e melhorar a sua eficiência. Esta área de estudo progrediu significativamente, tanto a nível de hardware, como de software. Apesar disso, os métodos desenvolvidos não têm nem acrescentado benefícios adicionais, nem melhorado significativamente a taxa de sucesso do processo de ablação. Existem várias razões para tal, e grande parte deve-se ao facto destes métodos de análise estarem incorporados nos sistemas de mapeamento e o seu software ser exclusivo. Isto leva a que não consigamos perceber como é que os algoritmos funcionam nos diferentes sistemas de mapeamento para comparar as suas diferenças e semelhanças. Devido a estes constrangimentos, os investigadores são compelidos a desenvolver os seus próprios métodos de análise e técnicas de mapeamento, o que leva à existência de uma multitude de métodos e técnicas de mapeamento que parecem ser diferentes entre si, resultando em informação ambígua e conflituosa no que diz respeito aos mecanismos da FA, e a conclusões distintas entre estudos. O sucesso do tratamento poderia aumentar se tivéssemos uma melhor compreensão dos métodos de análise e da sua aplicação no contexto da FA; perceber se os métodos apontam para o mesmo fenómeno de fibrilhação, se existe alguma correlação entre os métodos, e se a informação fornecida pelos mesmos é complementar ou redundante. Assim, o objectivo deste trabalho consistiu em implementar diferentes métodos para analisar os EGMs e a estrutura 3D da aurícula esquerda (AE) de doentes com FA, numa tentativa de responder às questões que motivaram a realização deste projecto. Em última análise, ao observar os mapas 3D da AE tendo uma melhor compreensão dos métodos, poderemos identificar com precisão as regiões na AE responsáveis por iniciar a FA, e ter mais conhecimento sobre os mecanismos responsáveis pela mesma. Desta forma, o processo de ablação poderá alcançar o seu potencial. Para este projecto, foram incluídos os mapas 3D electroanatómicos da AE de dez doentes com FA paroxística ou persistente do hospital de Santa Marta, recolhidos com o sistema de mapeamento CARTO 3. Cada ponto electroanatómico dos mapas inclui as 12 derivações do ECG, e os EGMs unipolares e bipolares registados com o cateter de mapeamento Pentaray de 20 pólos. Porém, apenas os EGMs bipolares foram incluídos na análise. Processaram-se os sinais bipolares e, devido a algumas limitações, foi possível apenas a implementação de dois métodos diferentes para os analisar: um no domínio da frequência – Frequência Dominante (FD) –, e outro no domínio da Teoria da Informação – a entropia de Shannon. De seguida, criaram-se três tipos de mapas 3D electroanatómicos da AE para cada doente: um de voltagem, cuja informação foi adquirida com o sistema de mapeamento, um de FD, e outro de entropia. A informação de cada mapa estava organizada segundo um padrão de cores. Observando os diferentes tipos de mapas da AE paralelamente, foi possível comparar os métodos, e perceber que tipo de informação cada um deles fornecia, numa tentativa de melhor compreender os mecanismos da FA. Foi possível observar em algumas regiões da AE, principalmente nos mapas de voltagem e de FD, a presença de “centros de activação” ou “centros de fibrilhação”, que poderão ser os gatilhos responsáveis por desencadear ou manter o mecanismo de fibrilhação. Para confirmar se de facto aquelas regiões eram os gatilhos de fibrilhação, seria necessário submeter os doentes ao processo de ablação e queimar essas zonas; e posteriormente acompanhar os doentes para observar os efeitos do procedimento e confirmar a hipótese. Contudo, dadas as limitações do trabalho e o facto desta área de investigação ser pouco explorada, é fulcral obter um maior número de estudo comparativos entre mais métodos de diferentes domínios e confirmar se apontam ou não para o mesmo fenómeno de fibrilhação. Apesar de terem sido implementados apenas dois métodos de análise dos EGMs, o projecto permitiu a comparação entre os mesmos, uma área de estudo por onde ainda há muito para investigar. Com mais conhecimento sobre os diferentes métodos, a sua aplicação, inter-relação e adequação no estudo dos mecanismos da FA e das propriedades electrofisiológicas desta patologia, é possível desenvolver procedimentos de ablação mais eficientes e selectivos, de forma a diminuir os riscos e aumentar a taxa de sucesso do tratamento.Atrial fibrillation (AF) is the most frequent cardiac arrhythmia in clinical practice and is described by rapid and irregular contractions of the atria. Despite catheter ablation (CA) being a well-established treatment for AF, it is sub-optimal, with a success rate of approximately 50 % after a single procedure, with some patients requiring multiple procedures to achieve long-term freedom from this pathology. This prompted the proposal and development of various quantitative electrogram (EGM)-based methods along with different mapping systems with their respective mapping techniques, to better understand the mechanisms responsible for initiating and maintaining AF, thus improving ablation outcomes. However, this diversification of methods and tools resulted in disperse and inconsistent data regarding the mechanisms of AF. This work consisted of employing two different methods to analyse the electrograms (EGM): dominant frequency (DF) and Shannon entropy (ShEn). From these EGMs, metrics were then extracted and displayed in colour-coded fashion on a 3D mesh of the left atrium (LA) from patients with paroxysmal or persistent AF. The two methods were compared to understand whether or not these indicated different phenomena/mechanisms, and if these could locate sites suspected of triggering and maintaining AF. The results, while not fully conforming to the literature, allowed the comparison between different EGM analysis methods, a field of study that requires further research. Overall, this project highlighted the limited data available within the topic, hindering our understanding of AF mechanisms and development of more effective and selective ablation procedures to avoid unnecessary complications, and ultimately improve the effects of the treatment's outcomes

Three-dimensional Multiscale Modelling and Simulation of Atria and Torso Electrophysiology

A better understanding of the electrical activity of the heart under physiological and pathological conditions has always been key for clinicians and researchers. Over the last years, the information in the P-wave signals has been extensively analysed to un-cover the mechanisms underlying atrial arrhythmias by localizing ectopic foci or high-frequency rotors. However, the relationship between the activation of the different areas of the atria and the characteristics of the P-wave signals or body surface poten-tial maps are still far from being completely understood. Multiscale anatomical and functional models of the heart are a new technological framework that can enable the investigation of the heart as a complex system. This thesis is centred in the construction of a multiscale framework that allows the realistic simulation of atrial and torso electrophysiology and integrates all the anatom-ical and functional descriptions described in the literature. The construction of such model involves the development of heterogeneous cellular and tissue electrophysiolo-gy models fitted to empirical data. It also requires an accurate 3D representation of the atrial anatomy, including tissue fibre arrangement, and preferential conduction axes. This multiscale model aims to reproduce faithfully the activation of the atria under physiological and pathological conditions. We use the model for two main applica-tions. First, to study the relationship between atrial activation and surface signals in sinus rhythm. This study should reveal the best places for recording P-waves signals in the torso, and which are the regions of the atria that make the most significant contri-bution to the body surface potential maps and determine the main P-wave characteris-tics. Second, to spatially cluster and classify ectopic atrial foci into clearly differenti-ated atrial regions by using the body surface P-wave integral map (BSPiM) as a bi-omarker. We develop a machine-learning pipeline trained from simulations obtained from the atria-torso model aiming to validate whether ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions, and whether new BSPiM could be correctly classified with high accuracy.En la actualidad, una mejor compresión de la actividad eléctrica del corazón en condi-ciones fisiológicas y patológicas es clave para médicos e investigadores. A lo largo de los últimos años, la información derivada de la onda P se ha utilizado para intentar descubrir los mecanismos subyacentes a las arritmias auriculares mediante la localiza-ción de focos ectópicos y rotores de alta frecuencia. Sin embargo, la relación entre la activación de distintas regiones auriculares y las características tanto de las ondas P como de la distribución de potencial en la superficie del torso está lejos de entenderse completamente. Los modelos cardíacos funcionales y anatómicos son una nueva he-rramienta que puede facilitar la investigación relativa al corazón entendido como sis-tema complejo. La presente tesis se centra en la construcción de un modelo multiescala para la simula-ción realista de la electrofisiología cardíaca tanto a nivel auricular como de torso, integrando toda la información anatómica y funcional disponible en la literatura. La construcción de este modelo implica el desarrollo, en base a datos experimentales, de modelos electrofisiológicos heterogéneos tanto celulares como tisulares. Así mismo, es imprescindible una representación tridimensional precisa de la anatomía auricular, incluyendo la dirección de fibras y los haces de conducción preferentes. Este modelo multiescala busca reproducir fielmente la activación auricular en condiciones fisiológi-cas y patológicas. Su uso se ha centrado fundamentalmente en dos aplicaciones. En primer lugar, estudiar la relación entre la activación auricular en ritmo sinusal y las señales en la superficie del torso. Este estudio busca definir la mejor ubicación para el registro de las ondas P en el torso así como determinar aquellas regiones auriculares que contribuyen fundamentalmente a la formación y distribución de potenciales super-ficiales así como a las características de las ondas P. En segundo lugar, agrupar y cla-sificar espacialmente los focos ectópicos en regiones auriculares claramente diferen-ciables empleando como biomarcador los mapas superficiales de integral de la onda P (BSPiM). Se ha desarrollado para ello una metodología de aprendizaje automático en la que las simulaciones obtenidas con el modelo multiescala aurícula-torso sirven de entrenamiento, permitiendo validar si los focos ectópicos cuyos BSPiMs son similares se agrupan de forma natural en regiones auriculares no intersectadas y si BSPiMs nue-vos podrían ser clasificados prospectivamente con gran precisión.Avui en dia, una millor comprenssió de l'activitat elèctrica del cor en condicions fisio-lògiques i patològiques és clau per a metges i investigadors. Al llarg dels últims anys, la informació derivada de l'ona P s'ha utilitzat per intentar descobrir els mecanismes subjacents a les arítmies auriculars mitjançant la localització de focus ectòpics i rotors d'alta freqüència. No obstant això, la relació entre l'activació de diferents regions auri-culars i les característiques tant de les ones P com de la distribució de potencial en la superfície del tors està lluny d'entendre's completament. Els models cardíacs funcionals i anatòmics són una nova eina que pot facilitar la recerca relativa al cor entès com a sistema complex. La present tesi es centra en la construcció d'un model multiescala per a la simulació realista de la electrofisiologia cardíaca tant a nivell auricular com de tors, integrant tota la informació anatòmica i funcional disponible en la literatura. La construcció d'aquest model implica el desenvolupament, sobre la base de dades experimentals, de models electrofisiològics heterogenis, tant cel·lulars com tissulars. Així mateix, és imprescindible una representació tridimensional precisa de l'anatomia auricular, in-cloent la direcció de fibres i els feixos de conducció preferents. Aquest model multies-cala busca reproduir fidelment l'activació auricular en condicions fisiològiques i pa-tològiques. El seu ús s'ha centrat fonamentalment en dues aplicacions. En primer lloc, estudiar la relació entre l'activació auricular en ritme sinusal i els senyals en la superfí-cie del tors. A més a més, amb aquest estudi també es busca definir la millor ubicació per al registre de les ones P en el tors, així com, determinar aquelles regions auriculars que contribueixen fonamentalment a la formació i distribució de potencials superfi-cials a l'hora que es caracteritzen les ones P. En segon lloc, agrupar i classificar espa-cialment els focus ectòpics en regions auriculars clarament diferenciables emprant com a biomarcador els mapes superficials d'integral de l'ona P (BSPiM). És per això que s'ha desenvolupat una metodologia d'aprenentatge automàtic en la qual les simulacions obtingudes amb el model multiescala aurícula-tors serveixen d'entrenament, la qual cosa permet validar si els focus ectòpics, llurs BSPiMs són similars, s'agrupen de for-ma natural en regions auriculars no intersectades i si BSPiMs nous podrien ser classifi-cats de manera prospectiva amb precisió.Ferrer Albero, A. (2017). Three-dimensional Multiscale Modelling and Simulation of Atria and Torso Electrophysiology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88402TESI

Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

Patienten mit Vorhofflimmern sind einem fünffach erhöhten Risiko für einen ischämischen Schlaganfall ausgesetzt. Eine frühzeitige Erkennung und Diagnose der Arrhythmie würde ein rechtzeitiges Eingreifen ermöglichen, um möglicherweise auftretende Begleiterkrankungen zu verhindern. Eine Vergrößerung des linken Vorhofs sowie fibrotisches Vorhofgewebe sind Risikomarker für Vorhofflimmern, da sie die notwendigen Voraussetzungen für die Aufrechterhaltung der chaotischen elektrischen Depolarisation im Vorhof erfüllen. Mithilfe von Techniken des maschinellen Lernens könnten Fibrose und eine Vergrößerung des linken Vorhofs basierend auf P Wellen des 12-Kanal Elektrokardiogramms im Sinusrhythmus automatisiert identifiziert werden. Dies könnte die Basis für eine nicht-invasive Risikostrat- ifizierung neu auftretender Vorhofflimmerepisoden bilden, um anfällige Patienten für ein präventives Screening auszuwählen. Zu diesem Zweck wurde untersucht, ob simulierte Vorhof-Elektrokardiogrammdaten, die dem klinischen Trainingssatz eines maschinellen Lernmodells hinzugefügt wurden, zu einer verbesserten Klassifizierung der oben genannten Krankheiten bei klinischen Daten beitra- gen könnten. Zwei virtuelle Kohorten, die durch anatomische und funktionelle Variabilität gekennzeichnet sind, wurden generiert und dienten als Grundlage für die Simulation großer P Wellen-Datensätze mit genau bestimmbaren Annotationen der zugrunde liegenden Patholo- gie. Auf diese Weise erfüllen die simulierten Daten die notwendigen Voraussetzungen für die Entwicklung eines Algorithmus für maschinelles Lernen, was sie von klinischen Daten unterscheidet, die normalerweise nicht in großer Zahl und in gleichmäßig verteilten Klassen vorliegen und deren Annotationen möglicherweise durch unzureichende Expertenannotierung beeinträchtigt sind. Für die Schätzung des Volumenanteils von linksatrialem fibrotischen Gewebe wurde ein merkmalsbasiertes neuronales Netz entwickelt. Im Vergleich zum Training des Modells mit nur klinischen Daten, führte das Training mit einem hybriden Datensatz zu einer Reduzierung des Fehlers von durchschnittlich 17,5 % fibrotischem Volumen auf 16,5 %, ausgewertet auf einem rein klinischen Testsatz. Ein Long Short-Term Memory Netzwerk, das für die Unterscheidung zwischen gesunden und P Wellen von vergrößerten linken Vorhöfen entwickelt wurde, lieferte eine Genauigkeit von 0,95 wenn es auf einem hybriden Datensatz trainiert wurde, von 0,91 wenn es nur auf klinischen Daten trainiert wurde, die alle mit 100 % Sicherheit annotiert wurden, und von 0,83 wenn es auf einem klinischen Datensatz trainiert wurde, der alle Signale unabhängig von der Sicherheit der Expertenannotation enthielt. In Anbetracht der Ergebnisse dieser Arbeit können Elektrokardiogrammdaten, die aus elektrophysiologischer Modellierung und Simulationen an virtuellen Patientenkohorten resul- tieren und relevante Variabilitätsaspekte abdecken, die mit realen Beobachtungen übereinstim- men, eine wertvolle Datenquelle zur Verbesserung der automatisierten Risikostratifizierung von Vorhofflimmern sein. Auf diese Weise kann den Nachteilen klinischer Datensätze für die Entwicklung von Modellen des maschinellen Lernens entgegengewirkt werden. Dies trägt letztendlich zu einer frühzeitigen Erkennung der Arrhythmie bei, was eine rechtzeitige Auswahl geeigneter Behandlungsstrategien ermöglicht und somit das Schlaganfallrisiko der betroffenen Patienten verringert

Doctor of Philosophy

dissertationFibrillation is defined as turbulent cardiac electrical activity and results in the inability of the myocardium to contract. When fibrillation occurs in the ventricles, it is known as ventricular fibrillation (VF). The consequence of VF is sudden death unless treated immediately. Fibrillation can also occur in the atria and is known as atrial fibrillation (AF). The consequences of atrial fibrillation (AF) are less immediate; however, it leads to increased risk of stroke. Despite the impact of fibrillatory arrhythmias, there are many gaps in our mechanistic knowledge of these arrhythmias. The purpose of this dissertation is to study through several projects how different cardiac substrates help initiate and/or sustain fibrillation. The first project examined several properties of the ventricular conduction system during VF. The conduction system coordinates excitation and consequently coordinates the contraction of the ventricles. Despite the conduction system's unique structure, its role in VF remains unclear. We examined the proximal conduction system and found that it develops a more rapid activation rate than the ventricular myocardium during prolonged VF, and may be driving the arrhythmia. The second and third projects examined the effects of fibrosis on electrical conduction to initiate and/or sustain AF. Despite fibrosis being associated with AF, it is still unknown whether it is a byproduct of an underlying heart disease and does not in itself promote AF, or if it affects the organization of conduction during fibrillation to promote AF. In the second project we studied the effect of fibrosis on conduction following different types of triggers. We found that fibrosis causes transverse conduction slowing following premature stimulation, which makes AF more likely to initiate. As AF persists, single episodes of AF last longer before the patient transitions into normal sinus rhythm, and in some cases AF can become permanent. The third project examined why some patients may never transition from AF to normal sinus rhythm. Specifically, this project found that regions of dense fibrosis anchor high-frequency activation that may be driving the arrhythmia. These studies showed that fibrosis causes conduction changes that make AF more likely to initiate and to be sustained