Relationship between body surface potential maps and atrial electrograms in patients with atrial fibrillation

PhD ThesisAtrial fibrillation (AF) is the most common cardiac arrhythmia. It is distinguished by fibrillating or trembling of the atrial muscle instead of normal contraction. Patients in AF have a much higher risk of stroke. AF is often driven by the left atrium (LA) and the diagnosis of AF is normally made from lead V1 in a 12-lead electrocardiogram (ECG). However, lead V1 is dominated by right atrial activity due to its proximal location to the right atrium (RA). Consequently it is not well understood how electrical activity from the LA contributes to the ECG. Studies of the AF mechanisms from the LA are typically based on invasive recording techniques. From a clinical point of view it is highly desirable to have an alternative, non-invasive characterisation of AF. The aim of this study was to investigate how the LA electrical activity was expressed on the body surface, and if it could be observed preferentially in different sites on the body surface. For this purpose, electrical activity of the heart from 20 patients in AF were recorded simultaneously using 64-lead body surface potential mapping (BSPM) and bipolar 10-electrode catheters located in the LA and coronary sinus (CS). Established AF characteristics such as amplitude, dominant frequency (DF) and spectral concentration (SC) were estimated and analysed. Furthermore, two novel AF characteristics (intracardiac DF power distribution, and body surface spectral peak type) were proposed to investigate the relationship between the BSPM and electrogram (EGM) recordings. The results showed that although in individual patients there were body surface sites that preferentially represented the AF characteristics estimated from the LA, those sites were not consistent across all patients. It was found that the left atrial activity could be detected in all body surface sites such that all sites had a dominant or non-dominant spectral peak corresponding to EGM DF. However, overall the results suggested that body surface site 22 (close to lead V1) was more closely representative of the CS activity, and site 49 (close to the posterior lower central right) was more closely representative of the left atrial activity. There was evidence of more accurate estimation of AF characteristics using additional electrodes to lead V1

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

Acoustic Radiation Force Impulse Imaging of Radiofrequency Ablation Lesions for Cardiac Ablation Procedures

<p>This dissertation investigates the use of intraprocedure acoustic radiation force impulse (ARFI) imaging for visualization of radiofrequency ablation (RFA) lesions during cardiac transcatheter ablation (TCA) procedures. Tens of thousands of TCA procedures are performed annually to treat atrial fibrillation (AF) and other cardiac arrhythmias. Despite the use of sophisticated electroanatomical mapping (EAM) techniques to validate the modification of the electrical substrate, post-procedure arrhythmia recurrence is common due to incomplete lesion delivery and electrical conduction through lesion line discontinuities. The clinical demand for an imaging modality that can visually confirm the presence and completeness of RFA lesion lines motivated this research.</p><p>ARFI imaging is an ultrasound-based technique that transmits radiation force impulses to locally displace tissue and uses the tissue deformation response to generate images of relative tissue stiffness. RF-induced heating causes irreversible tissue necrosis and contractile protein denaturation that increases the stiffness of the ablated region. Preliminary in vitro and in vivo feasibility studies determined RF ablated myocardium appears stiffer in ARFI images.</p><p>This thesis describes results for ARFI imaging of RFA lesions for three research milestones: 1) an in vivo experimental verification model, 2) a clinically translative animal study, and 3) a preliminary clinical feasibility trial in human patients. In all studies, 2-D ARFI images were acquired in normal sinus rhythm and during diastole to maximize the stiffness contrast between the ablated and unablated myocardium and to minimize the bulk cardiac motion during the acquisition time.</p><p>The first in vivo experiment confirmed there was a significant decrease in the measured ARFI-induced displacement at ablation sites during and after focal RFA; the displacements in the lesion border zone and the detected lesion area stabilized over the first several minutes post-ablation. The implications of these results for ARFI imaging methods and the clinical relevance of the findings are discussed.</p><p>The second and third research chapters of this thesis describe the system integration and implementation of a multi-modality intracardiac ARFI imaging-EAM system for intraprocedure lesion evaluation. EAM was used to guide the 2-D ARFI imaging plane to targeted ablation sites in the canine right atrium (RA); the presence of EAM lesions markers and conduction disturbances in the local activation time (LAT) maps were used to find the sensitivity and specificity of predicting the presence of RFA lesion with ARFI imaging. The contrast and contrast-to-noise ratio between RFA lesion and unablated myocardium were calculated for ARFI and conventional ICE images. The opportunities and potential developments for clinical translation are discussed. </p><p>The last research chapter in this thesis describes a feasibility study of intracardiac ARFI imaging of RFA lesions in clinical patients. ARFI images of clinically relevant ablation sites were acquired, and this pilot study determined ARFI-induced displacements in human myocardium decreased at targeted ablation sites after RF-delivery. The challenges and successes of this pilot study are discussed.</p><p>This work provides evidence that intraprocedure ARFI imaging is a promising technology for the visualization of RFA lesions during cardiac TCA procedures. The clinical significance of this research is discussed, as well as challenges and considerations for future iterations of this technology aiming for clinical translation.</p>Dissertatio

Modelling and Estimation of Spatiotemporal Cardiac Electrical Dynamics

The heart is a complex biological system in which electrical activation signals initiate at the pacemaker cells, propagate through the heart tissue to both trigger and synchronise the mechanical contractions. Abnormalities in the cardiac electrical signals lead to dangerous cardiac arrhythmias. Therefore, understanding the functionalities of the cardiac electrical activity is essential for the development of novel techniques to facilitate advanced diagnosis and treatment for arrhythmia. By combining experimental or clinical electrophysiology data with mathematical models, system theoretic approaches can be used to provide quantitative insights into the normal and pathological mechanisms of the cardiac electrical activity. This thesis proposes model-based estimation methods to reconstruct and quantify the underlying spatiotemporal cardiac electrical dynamics from the cardiac electrogram measurements. Firstly, a statistical model-based estimation framework is proposed to reconstruct the tissue dynamics from the cardiac electrogram measurements. The reconstruction of the tissue dynamics is based on an integrated model of cardiac electrical activity, which incorporates the cardiac action potential dynamics at the cell-level, tissue-level and extracellular-level. The dynamics of the cardiac tissue is described using the monodomain tissue model, which is coupled with the continuous version of modified Mitchell-Schaeffer model. The resulting model equations are of infinite-dimensional form, which is converted into a finite-dimensional state-space representation via a model reduction method. In order to estimate the hidden state variables of the tissue dynamics from the cardiac electrogram measurements, a combined detection-estimation framework using a single filter unscented-transform based smoothing algorithm is proposed. The detection step in the proposed method enables the inclusion of localised stimulus events into the model-based estimation framework. The performance of the proposed algorithms are demonstrated using the modelled cardiac activation patterns of normal and reentrant conditions, in both one-dimensional and two-dimensional tissue field. The findings from this proposed study illustrate that the hidden state variables of the tissue model can be estimated from the electrogram measurements, simultaneously by detecting the stimulus events. Therefore, this method shows that the complex spatiotemporal cardiac activity can be reconstructed from the coarse electrograms using the state estimation methods. Secondly, a complex network modelling approach is proposed to quantify the spatiotemporal organisation of electrical activation during human ventricular fibrillation. The proposed network modelling approach includes three different methods based on correlation analysis, graph theoretical measures and hierarchical clustering. Using the proposed approach, the level of spatiotemporal organisation is quantified during three episodes of VF in ten patients, recorded using multi-electrode epicardial recordings with 30 s coronary perfusion, 150 s global myocardial ischaemia and 30 s reflow. The findings show a steady decline in spatiotemporal organisation from the onset of VF with coronary perfusion. Following this, a transient increases in spatiotemporal organisation is observed during global myocardial ischaemia. However, the decline in spatiotemporal organisation continued during reflow. The results are consistent across all patients, and are consistent with the numbers of phase singularities. The findings show that the complex spatiotemporal patterns can be studied using complex network analysis

Multichannel Intracardiac Electrogram Analysis to Estimate the Depolarisation Wavefront Propagation: Supporting Diagnostics and Treatment of Atrial Fibrillation

Kardiale Arrhythmien sind Störungen des Herzrhythmus, welche von unregelmäßigem Herzschlag kommen. Vorhofflimmern ist die am weitesten verbreitete Herzrhythmusstörung und ist mit zunehmendem Alter weiter verbreitet. Thromboembolische Ereignisse und Störungen der Hämodynamik können als Begleiterscheinungen von Vorhofflimmern (AFib) auftreten und eine signifikant gesteigerte Morbidität und Mortalität zur Folge haben. Die Be- handlung von AFib erfolgt mit Medikamenten und zudem mit Hilfe der Katheterablation. Im Zuge der Ablation versuchen Ärzte die Bereiche arrhythmogenen Substrats zu lokalisieren. Danach werden kleine Ablationsnarben im Herzgewebe erzeugt, welche die Ausbreitung abnormaler elektrischer Erregungen im Herzen unterdrücken sollen. Die Erfolgsraten dieser Prozedur erreichen bis zu 70% nach zwei oder drei Ablationen. Im Zuge diese Arbeiten wurden die Regionen arrhythmogenen Substrats lokalisiert, und die Details der Erregungsausbreitung über dieses Substrat wurden bestimmt. Im Verlauf dieser Arbeit wurden klinische Daten, experimentelle Daten und Simulationen für die Analyse genutzt. Simulationen wurden genutzt um die lokale Aktivierungszeit (LAT) auf klinischen Anatomien zu bestimmen. Experimentelle Daten wurden mit Hilfe eines Elektrodenpatches von einem Hund herzen erfasst. Klinische Daten wurden mit Hilfe eines elektroanatomischen Mappingsystems im Rahmen klinischer Routineuntersuchungen aufgezeichnet. Die aufgezeichneten Daten wurden einer Vorverarbeitung unterzogen um messtechnische und geometrische Artefakte wie das ventrikuläre Fernfeld (VFF) oder hoch- und niederfrequentes Rauschen zu unterdrücken. Eine Vielzahl von Merkmalen wurden aus den vorbearbeiteten Daten gewonnen. Dies waren die Bestimmung des Stimulationsprokotolls, die Abschätzung der Dauer der fraktionierten Aktivität, die Korrelation der Morphologie, Spitzen-zu-Spitzen Amplitude, Bestimmung der QRS Komplexe, lokale Aktivierungszeit, die Bestimmung einer stabilen Katheterposition und die Markierung der Region des arrhythmogenen Substrats. Die Methode zur Bestimmung von Richtung und Geschwindigkeit der Erregungsausbreitung wurde bestimmt. Ein grafisches Nutzerinterface (GUI) wurde entwickelt zur Bestimmung der Ausbreitungsgeschwindigkeit und darauf basierender regionaler Analyse. Simulierte Daten wurden genutzt um die Leistungsfähigkeit der entwickelten Algorithmen zu beurteilen. Zur Simulation der LAT auf klinischen Anatomien wurde die fast marching Methode (FaMaS) genutzt. In diesen Simulationen war die goldene Wahrheit für eine Beurteilung der Parameterabschätzung bekannt. Ein umsichtiger und erfolgreicher Versuch wurde unternommen, um Muster und Geschwindig- keit der Erregungsausbreitung auf dem Vorhof zu bestimmen. Dies wurde auf Basis der LAT Zeit und stabiler Katheterpositionen durchgeführt. Interessante Regionen wurden zudem als wahrscheinliche Regionen eines arrhythmogenen Substrats im linken Vorhof markiert. Dies wurde auf Grundlage mehr als eines Merkmals und visueller Beurteilung deren Verteilung im Vorhof durchgeführt. Für die stimulierten Daten wurde die Aktivität der S1 und S2 Erregung verglichen um Änderungen in der Erregungsausbreitung abzuschätzen. Die Auswertung der experimentellen Daten wurde in Kooperation mit internationalen Part- nern aus den USA durchgeführt. Für verschiedene Szenarien wurden dabei Richtung und Muster der Erregungsausbreitung abgeschätzt. Die zeitliche und räumliche Informationen der vorgeschlagenen Method war dabei genau kontrolliert. Mit den Auswertemethoden aus dieser Arbeit können die wahrscheinliche Region des arrhythmogenen Substrats und der Verlauf der Erregungsausbreitung auf dem Vorhof für Vorhofflimmern und Vorhofflattern bestimmt werden. Diese können dem behandelnden Arzt bei der Planung der Ablationstherapie und erfolgreicher Durchführung helfen

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

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

Do changes in the expression of Gαi2 affect cardiac electrophysiology?

PhDThe heterotrimeric G protein subunit, Gαi2, is involved in signal transduction from muscarinic acetylcholine and other receptor systems in cardiomyocytes. Gαi2 expression is elevated in human heart failure, though whether this is beneficial or maladaptive remains unknown. Better understanding could guide therapeutics development. Previous work with Gαi2 knockout mice suggested a pro-arrhythmic phenotype. We hypothesised that increased Gαi2 expression is anti-arrhythmic in the ventricles. To investigate this, an in vivo murine model of myocardial infarction was used to approximate the human pathophysiology, with wild-type (WT) mice compared to those with cardiospecific Gαi2 knockout. Subsequently, an ex vivo model of cardiac tissue slices was used to evaluate normal electrophysiological properties of murine ventricular tissue, alterations with β-adrenoceptor and muscarinic agonists and temperature, and comparison of these properties in WT mice and those with Gαi2 globally deleted. With the myocardial infarction model, there were no significant cardiac phenotypic differences between cardiospecific knockouts and WTs. The cardiac slice model, which utilised a micro-electrode array, showed stable activation and repolarisation properties in WT slices. Comparison of WTs to Gαi2 global knockouts in the presence of carbachol found no significant differences between groups in terms of repolarisation or conduction properties. In WT slices, isoprenaline was associated with a small increase in effective refractory period, but did not alter conduction properties. There was a highly significant negative linear relationship between temperature and both activation, and repolarisation. Murine models were used to investigate the electrophysiological effects of autonomic signalling pathways, and in particular, the protein Gαi2. No observable electrophysiological differences between WT and Gαi2 knockout mice were demonstrated. β-adrenergic agonism produced small changes in repolarisation only. Effects of temperature on activation and refractoriness suggest modulation of sodium and potassium currents, in keeping with published work. These findings contribute to our understanding of autonomic modulation of murine cardiac electrophysiolog

Novel approaches for quantitative electrogram analysis for rotor identification: Implications for ablation in patients with atrial fibrillation

University of Minnesota Ph.D. dissertation. May 2017. Major: Biomedical Engineering. Advisor: Elena Tolkacheva. 1 computer file (PDF); xxviii, 349 pages + 4 audio/video filesAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US. Catheter ablation with pulmonary vein isolation (PVI) to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of PVI region. Recent evidences in the mechanistic understating of AF pathophysiology suggest that ectopic activity, localized re-entrant circuit with fibrillatory propagation and multiple circuit re-entries may all be involved in human AF. Accordingly, the hypothesis that rotor is an underlying AF mechanism is compatible with both the presence of focal discharges and multiple wavelets. Rotors are stable electrical sources which have characteristic spiral waves like appearance with a pivot point surrounded by peripheral region. Targeted ablation at the rotor pivot points in several animal studies have demonstrated efficacy in terminating AF. The objective of this dissertation was to develop robust spatiotemporal mapping techniques that can fully capture the intrinsic dynamics of the non-stationary time series intracardiac electrogram signal to accurately identify the rotor pivot zones that may cause and maintain AF. In this thesis, four time domain approaches namely multiscale entropy (MSE) recurrence period density entropy (RPDE), kurtosis and intrinsic mode function (IMF) complexity index and one frequency domain approach namely multiscale frequency (MSF) was proposed and developed for accurate identification of rotor pivot points. The novel approaches were validated using optical mapping data with induced ventricular arrhythmia in ex-vivo isolated rabbit heart with single, double and meandering rotors (including numerically simulated data). The results demonstrated the efficacy of the novel approaches in accurate identification of rotor pivot point. The chaotic nature of rotor pivot point resulted in higher complexity measured by MSE, RPDE, kurtosis, IMF and MSF compared to the stable rotor periphery that enabled its accurate identification. Additionally, the feasibility of using conventional catheter mapping system to generate patient specific 3D maps for intraprocedural guidance for catheter ablation using these novel approaches was demonstrated with 1055 intracardiac electrograms obtained from both atria’s in a persistent AF patient. Notably, the 3D maps did not provide any clinically significant information on rotor pivot point identification or the presence of rotors themselves. Validation of these novel approaches is required in large datasets with paroxysmal and persistent AF patients to evaluate their clinical utility in rotor identification as potential targets for AF ablation