Characterisation of atrial flutter variants based on the analysis of spatial vectorcardiographic trajectory from standard ECG

After atrial fibrillation, atrial flutter is the most common atrial tachyarrhythmia. Its diagnosis relies on the twelve lead electrocadriogram analysis of the distinctive waves in several leads. Nonetheless, the accurate identification of the type of atrial flutter still requires an invasive procedure. The maneuver for healing atrial flutter consists on ablating a section of the anatomy of the atria, to stop the macroreentrant circuit to keep happening, allowing the signal to travel to the ventricles in stead of staying at the atria. The region to ablate directly depends on the place at which the macroreentrant circuit is located, which at the same time depends on the type of atrial flutter. Being able to noninvasively detect the atrial flutter variant would produce a great advantage when healing this illness. The hypothesis stated at this dissertation is based on the slow conduction regions as the key factor to distinguish the atrial flutter class. This and unveiling further relations between cardiac illnesses and their signal’s alter ego are the purpose of this research project. With such aim, different methods are developed based on the vectorcardiographic representation of electrocardiograms from patients suffering from different atrial flutter types. These methods consist on the characterisation of vectorcardiographic signals from different standpoints. Besides, a mathematical model is implemented to create a large database with synthetic vectorcardiographic signals allowing to test the validity of the utilised methods. The results prove the importance of slow regions in the vectorcardiographic representation of the patient’s signals to characterise the atrial flutter type non-invasively. Furthermore, the analysis of the outcome of the different methods reveal a wide variety of features relating characteristics of the vectorcardiographic signal to the anatomy and physiology of this cardiac disease. Hence, not only results supporting the hypothesis were successful (taking into account some limitations), but also a variegated assortment of results unmasked remarkable relations among the vectorcardiographic signal and the characteristics of the atrial flutter disease.Ingeniería Biomédic

Activation patterns in atrial fibrillation: contributions of body surface potential mapping

La fibrilación auricular (FA) es una de las arritmias cardiacas más comunes, afectando a alrededor del 10 % de los mayores de 70 años. A pesar de su alta incidencia en la población, los mecanismos que desencadenan y mantienen la FA son inciertos. Aunque existen diversos tratamientos quirúrgicos y farmacológicos, el éxito de los tratamientos contra la FA es muy bajo. La causa de esta baja tasa de éxito de las diferentes terapias es que no existen criterios de selección de pacientes que permitan pronosticar qué terapia puede ser más efectiva para cada paciente. Una de las formas que se han propuesto para determinar el grado de gravedad de la arritmia en cada paciente y, por tanto, poder predecir qué tratamiento es el más apropiado es la medida de la organización auricular. Esta tesis doctoral se enmarca dentro de la determinación no invasiva del grado de organización espacial de la activación del miocardio auricular a partir del estudio de registros multiderivación del electrocardiograma de superficie (ECG). El ECG es una representación simplificada del campo eléctrico del corazón basada en las proyecciones de este campo eléctrico en 8 ejes. Esta simplificación es considerada como aceptable en el caso de ritmos no fibrilantes en los que la activación miocárdica puede ser modelada como un dipolo. Sin embargo, su validez no ha sido demostrada para el caso de ritmos fibrilantes en los cuales la asunción de un modelo dipolar es cuestionable. Uno de los objetivos de esta tesis ha sido la evaluación del electrocardiograma de superficie para la obtención de parámetros espaciales de las ondas de FA. Se compararon las representaciones tridimensionales de las ondas de FA registradas a partir de tres derivaciones ortogonales con las representaciones tridimensionales estimadas a partir del ECG, llegando a la conclusión de que estas representaciones estimadas no son fieles a las representaciones registradas. Los resultados de nuestro estudio ponen de manifiesto que la falta de dGuillem Sanchez, MDLS. (2008). Activation patterns in atrial fibrillation: contributions of body surface potential mapping [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3922Palanci

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

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This thesis presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This book presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This book presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF

The Application of Computer Techniques to ECG Interpretation

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