Directed networks as a novel way to describe and analyze cardiac excitation : directed graph mapping

Networks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proof-of-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow determination of the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve the accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood

A multi-variate predictability framework to assess invasive cardiac activity and interactions during atrial fibrillation

Objective: This study introduces a predictability framework based on the concept of Granger causality (GC), in order to analyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). Methods: GC-based interactions were studied using a three-electrode analysis scheme with multi-variate autoregressive models of the involved preprocessed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial signals acquired from patients. Results: The results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. Conclusion: The proposed framework provides information on the underlying activation and regularity, does not require activation detection or postprocessing algorithms and is applicable for the analysis of any multielectrode catheter. Significance: The proposed framework can potentially help to guide catheter ablation interventions of AF

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

Detección de patrones de activación eléctrica cardiaca mediante medidas de causalidad

The atrial fibrillation is one of the most common cardiac arrhythmia. It consists on an irregular propagations of the electric activity in the cardiac tissue. This anomaly causes inefficiency in the physiological activity of the atrium and reduces the quality of patient`s life. The unknowledge of initiation mechanism and maintenance of the arrhythmia make currently therapies have a low success rate. Cardiac ablation is the most common therapy, in this process the heart tissue¿s area which causes the singularity is burnt by radiofrequency. In the last few years a new method has been developed. It is based on causality relationships and processing cardiac signal it is possible to predict the hierarchically dominant area driving atrial fibrillation. This technique is pretend to use to guide a catheter ablation surgery, to this propose in this project, the method is implemented on a multiplatform language, C++. A Static library has been created and it allow execute the program in any platform with the goal to commercialize it. This program has been validated with mathematic cardiac signal models.La fibrilación auricular es la arritmia cardiaca más frecuente y consiste en una propagación irregular de la actividad eléctrica en el tejido cardiaco. Esta anomalía provoca una ineficiencia en la actividad mecánica de la aurícula y reduce en gran medida la calidad de vida del paciente. El desconocimiento del mecanismo de inicio y mantenimiento de la arritmia hacen que en la actualidad las terapias utilizadas para su extinción tengan una baja tasa de éxito. Una de las terapias más utilizadas es la ablación cardiaca que consiste en quemar por radiofrecuencia la zona del tejido cardiaco que puede estar provocando esta arritmia. En los últimos años se ha desarrollado un novedoso método en el que a partir del tratamiento de la señal eléctrica cardiaca, aplicándole el principio de causalidad se predice cuál es la zona jerárquicamente dominante en la actividad fibrilatoria. Esta técnica se pretende aplicar para el guiado en la intervención de ablación por catéter. Con dicho objetivo, en este proyecto el método es implementado en un lenguaje multiplataforma, C++ y validado mediante señales provenientes de métodos matemáticos. El resultado del proyecto es una librería estática que resuelve en tiempo real el problema de causalidad aplicado a la fibrilación auricular, lo que permitirá su uso en la práctica clínica.Tomás González, E. (2016). Detección de patrones de activación eléctrica cardiaca mediante medidas de causalidad. http://hdl.handle.net/10251/92569TFG

Análisis de la causalidad de la fibrilación auricular en registros de mapeo de alta densidad

La fibrilación auricular (FA) es la arritmia mas común, siendo responsable de un tercio de las hospitalizaciones en las unidades de arritmias, tanto en Europa como en EEUU. Su alta incidencia, sobre todo en la población de mayor edad, la convierte en un problema de salud general en continuo estudio. Sin embargo, aún no se conocen todos los mecanismos que la promueven y sostienen, siendo por tanto su tratamiento actual, poco efectivo. En este TFG se implementará y evaluará un método para cuantificar las relaciones causales entre señales EGM procedentes de arrays de alta densidad de electrodos durante fibrilación auricular

Propagation pattern analysis during atrial fibrillation based on sparse modeling

Abstract in UndeterminedIn this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using simulated data, both optimization methods were superior to LS estimation with respect to detection and estimation performance. The normalized error between the true and estimated model parameters dropped from 0.20 +/- 0.04 for LS estimation to 0.03 +/- 0.01 for both aLASSO and dLASSO when the number of available data samples exceeded the number of model parameters by a factor of 5. For shorter data segments, the error reduction was more pronounced and information on the distance gained in importance. Propagation pattern analysis was also studied on intracardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption

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

Multichannel Analysis of Intracardiac Electrograms - Supporting Diagnosis and Treatment of Cardiac Arrhythmias

Cardiologists diagnose and treat atrial tachycardias using electroanatomical mapping systems. These can be combined with multipolar catheters to record intracardiac electrograms. Within this thesis, various signal processing techniques were implemented and benchmarked to analyze electrograms. They support the physician in diagnosis and treatment of atrial flutter and atrial fibrillation. The developed methods were assessed using simulated data and demonstrated on clinical cases