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

Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation

Clinical decision support systems (clinical DSSs) are widely used today for various clinical applications such as diagnosis, treatment, and recovery. Clinical DSS aims to enhance the end‐to‐end therapy management for the doctors, and also helps to provide improved experience for patients during each phase of the therapy. The goal of this chapter is to provide an insight into the clinical DSS associated with the highly prevalent heart rhythm disorder, atrial fibrillation (AF). The use of clinical DSS in AF management is ubiquitous, starting from detection of AF through sophisticated electrophysiology treatment procedures, all the way to monitoring the patient\u27s health during follow‐ups. Most of the software associated with AF DSS are developed based on signal processing, image processing, and artificial intelligence techniques. The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. The chapter continues with a background on AF and some relevant mechanisms. Finally, a couple of clinical DSS used today in regard with AF are discussed, along with some proposed methods for potential implementation of clinical DSS for detection of AF, prediction of an AF treatment outcome, and localization of AF targets during a treatment procedure

Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology

The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals

Characterization of Cardiac Electrogram Signals During Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia in United States. The most popular treatment for AF is a percutaneous procedure called catheter ablation. Current AF ablation procedures unfortunately have a poor success rate, primarily because the mechanisms involved in AF are incompletely understood even today. Intra-atrial electrograms have previously been shown to provide information on the mechanisms of AF. This thesis focuses on two such mechanisms – AF-sustaining sites known as sustained rotational activities (RotAs), and atrial tissue with unique electrical properties known as myocardial scars. Catheter ablation procedures today construct the 3D electroanatomic map of the left atrium (LA) by maneuvering a conventional Multipolar Diagnostic Catheter (MPDC) along the LA endocardial surface. These procedures are limited to pulmonary vein isolation and other linear ablation performed on various regions of the left atrium (such as roof and mitral isthmus) where the regions are decided based on the atrial anatomy. However, it remains unclear how to utilize the information provided by the MPDC to analyze and characterize the RotAs and scars. Previous electrogram characterization studies mainly use a single bipole rather than MPDCs to characterize the electrograms based on features such as cycle length or dominant frequency from the time or frequency domain. In this thesis we developed novel techniques for investigating the above mentioned mechanisms using signal analysis, mathematical modeling, numerical simulation and clinical experiments, all utilizing MPDC recordings. First, the variations in the total conduction delay (TCD) from MPDC electrograms as the MPDC moves towards a RotA source was investigated. Second, the maximum peak-to-peak amplitudes of MPDC electrograms recorded during AF and NSR were analyzed. This thesis provides insights into methods of characterization of cardiac electrograms and the findings of this thesis could address the current challenges in AF ablation

Intracardiac organization indices for the monitoring of atrial fibrillation

Atrial fibrillation (AF) is the most common arrhythmia observed in clinical practice. It is responsible for about one third of hospitalizations related to problems of arrhythmia. AF is an important clinical entity due to the increased risk of morbidity and mortality. The consequences of AF most frequently found are hemodynamic function impairment (loss of atrial synchronized contraction, irregular and inadequately rapid ventricular rate), atriogenic thromboembolic events and tachycardia induced atrial and ventricular cardiomyopathy. With the present increase of life expectancy, AF prevalence is expected to double in the next fifty years, in particular in western countries. In collaboration with the Division of Cardiology of CHUV, a catheter ablative protocol mainly based on pulmonary vein isolation (PVI) and complex fractionated elctrograms ablations was defined in order to develop new strategies to decrease procedural time and ablation extent. More precisely, surface EGC as intracardiac electrogram (EGM) signals were recorded from different catheters at specific locations before ablation during and after PVI. The purpose of this project is to evaluate the ability of known (AF cycle length) and new intracardiac organization indices based on recorded surface ECGs and EGM signals to monitor AF organization during stepwise ablation of persistent AF

New perspectives in catheter ablation for atrial fibrillation Towards a better treatment to reach better outcomes

The overall aim of the studies presented in this thesis is to elucidate whether there is still room for improvement in the field of catheter ablation for AF either paroxysmal and persistent, and the following chapters will guide the reader in a virtual path that addresses this issue

Novel ECG and Intracardiac Electrograms Signal Processing Schemes for Predicting the Outcome of Atrial Fibrillation Catheter Ablation

Atrial fibrillation (AF) is the most common encountered cardiac rhythm disorder (arrhythmia) in clinical practice. It is responsible for about one third of arrhythmia-related hospitalizations. This arrhythmia, which increases in prevalence with age, leads to severe complications and subsequently decreases the quality of life for the affected patients. Lifetime risks for developing AF are ~25% in subjects older than 40 years old. Currently, this arrhythmia is considered as a major public health concern. AF is a progressive disease, starting by short and rare episodes which further develop into longer and more frequent occurrences. When the arrhythmia becomes sustained for more than one year, it is labelled as long-standing persistent. AF advancement gives rise to an electrical of the atria (the upper chambers of the heart) resulting from abnormal high frequency atrial activations. The main goals of therapeutic management for patients with AF are to prevent severe complications associated with this arrhythmia, and ultimately to restore a normal rhythm. Currently, the cornerstone of non-pharmacological therapy is the radiofrequency catheter ablation of AF, which consists in delivering at strategic locations within the atria high-frequency electrical impulses. However, catheter ablation for patients with long-standing persistent AF involves extensive ablation of the atria and the success rate reported in various publications is associated with conflicting results. Over the last twenty years, an important effort has been made by the scientific community to develop signal processing algorithms to quantify the complexity of temporal or spectral characteristics of AF dynamics in terms of organization. As such, multiple approaches have been proposed to quantify AF organization either based on time-domain or frequency-domain analysis. All these methods shared one common goal: the development of organization indices which are interpretable from an electrophyisiological viewpoint. In the context of catheter ablation of patients with long-standing persistent AF, the success rate appears limited as the "classical" organization indices are not performant in assessing the amount of ablation required to achieve AF termination. Thus, there is a strong interest in predicting the procedural outcome from the surface electrocardiogram (ECG) recorded at baseline, i.e., prior to ablation. The main objective of this thesis was to derive novel organization indices from surface ECG and intracardiac signals acquired at baseline which could discriminate patients in whom AF was terminated from patients in whom AF persisted during catheter ablation within the left atrium. As the standard surface ECG is not appropriate for measuring the atrial activity, we aimed at adapting the placement of at least one ECG lead such that additional electrical information from the atria was provided. In our ECG signals study, we hypothesized that a quantification of the harmonic structure of AF signals brings more insight into AF complexity. Time-invariant and time-varying approaches were used to derive the ECG organization indices, and their performance for predicting the acute outcome of catheter ablation were compared. In the first scheme, the harmonic components of AF waves were extracted using linear time-invariant filters. In the second one, the components were extracted using an adaptive harmonic frequency tracking algorithm. [...

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at https://doi.org/10.1088/1361-6579/ab97c1.[EN] Objective: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. Approach: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3¿30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction in the number of leads. A total of 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the highest DFBSPM (HDFBSPM) with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ¿1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanism locations. Torso HDF regions, defined as portions of the maps with |DF ¿ HDFBSPM| ¿ 0.5 Hz were identified and the percentage of the torso occuping these regions was compared between methods. The robustness of both methods to white Gaussian noise, ventricular influence and harmonics, and to lower spatial resolution BSPM lead layouts was analyzed: computer AF models (567 leads vs 256 leads down to 16 leads) and patient data (67 leads vs 32 and 16 leads). Main results: The proposed method allowed an improvement in non-invasive estimation of the atria HDF. For the models the median relative errors were 7.14% for the wavelet-based algorithm vs 60.00% for the Welch method; in patients, the errors were 10.03% vs 12.66%, respectively. The wavelet method outperformed the Welch approach in correct estimations of atrial HDFs in models (81.82% vs 45.45%, respectively) and patients (66.67% vs 41.67%). A low positive BSPM-DF map correlation was seen between the techniques (0.47 for models and 0.63 for patients), highlighting the overall differences in DF distributions. The wavelet-based algorithm was more robust to white Gaussian noise, residual ventricular activity and harmonics, and presented more consistent results in lead layouts with low spatial resolution. Significance: Estimation of atrial HDFs using BSPM is improved by the proposed wavelet-based algorithm, helping to increase the non-invasive diagnostic ability in AF.This study was supported in part by grants from Sao Paulo Research Foundation (2017/19775-3), Instituto de Salud Carlos III FEDER (Fondo Europeo de Desarrollo Regional PI17/01106) and Generalitat Valenciana Grants (AICO/2018/267).Marques, V.; Rodrigo Bort, M.; Guillem Sánchez, MS.; Salinet, J. (2020). A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals. Physiological Measurement. 41(7):1-14. https://doi.org/10.1088/1361-6579/ab97c1S11441

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

Hierarchical algorithms for causality retrieval in atrial fibrillation intracavitary electrograms

Multi-channel intracavitary electrograms (EGMs), are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation (AF). These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their cause-effect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches