Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation

[EN] Introduction Ablation of high dominant frequency (DF) sources in patients with atrial fibrillation (AF) is an effective treatment option for paroxysmal AF. The aim of this study was to evaluate the accuracy of noninvasive estimation of DF and electrical patterns determination by solving the inverse problem of the electrocardiography. Methods Four representative AF patients with left-to-right and right-to-left atrial DF patterns were included in the study. For each patient, intracardiac electrograms from both atria were recorded simultaneously together with 67-lead body surface recordings. In addition to clinical recordings, realistic mathematical models of atria and torso anatomy with different DF patterns of AF were used. For both mathematical models and clinical recordings, inverse-computed electrograms were compared to intracardiac electrograms in terms of voltage, phase, and frequency spectrum relative errors. Results Comparison between intracardiac and inverse computed electrograms for AF patients showed 8.8 ± 4.4% errors for DF, 32 ± 4% for voltage, and 65 ± 4% for phase determination. These results were corroborated by mathematical simulations showing that the inverse problem solution was able to reconstruct the frequency spectrum and the DF maps with relative errors of 5.5 ± 4.1%, whereas the reconstruction of the electrograms or the instantaneous phase presented larger relative errors (i.e., 38 ± 15% and 48 ± 14 % respectively, P < 0.01). Conclusions Noninvasive reconstruction of atrial frequency maps can be achieved by solving the inverse problem of electrocardiography with a higher accuracy than temporal distribution patterns.Pedrón-Torrecilla, J.; Rodrigo Bort, M.; M. Climent, A.; Liberos, A.; Pérez-David E; Bermejo, J.; Arenal, A.... (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology. 27(4):435-442. doi:https://doi.org/10.1111/jce.12931S43544227

Using Machine Learning to Characterize Atrial Fibrotic Substrate from Intracardiac Signals with a Hybrid in silico and in vivo Dataset

[EN] In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) through DO637/22-3, LO2093/1-1 and LU 2294/1-1, by the European Union's Horizon 2020 programme (grant agreement No.766082, MY-ATRIA project), by the KIT-Publication Fund of the Karlsruhe Institute of Technology and by the Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020 from the Ministerio de Ciencia e Innovacion y Universidades (PID2019-104356RB-C41/AEI/10.13039/501100011033)Sánchez Arciniegas, JP.; Luongo, G.; Nothstein, M.; Unger, LA.; Saiz Rodríguez, FJ.; Trenor Gomis, BA.; Luik, A.... (2021). Using Machine Learning to Characterize Atrial Fibrotic Substrate from Intracardiac Signals with a Hybrid in silico and in vivo Dataset. Frontiers in Physiology. 12:1-15. https://doi.org/10.3389/fphys.2021.699291S1151

Formation of Intracardiac Electrograms under Physiological and Pathological Conditions

This work presents methods to advance electrophysiological simulations of intracardiac electrograms (IEGM). An experimental setup is introduced, which combines electrical measurements of extracellular potentials with a method for optical acquisition of the transmembrane voltage in-vitro. Thereby, intracardiac electrograms can be recorded under defined conditions. Using experimental and clinical signals, detailed simulations of IEGMs are parametrized, which can support clinical diagnosis

Simulation of intracardiac electrograms around acute ablation lesions

Radiofrequency ablation (RFA) is a widely used clinical treatment for many types of cardiac arrhythmias. However, nontransmural lesions and gaps between linear lesions often lead to recurrence of the arrhythmia. Intracardiac electrograms (IEGMs) provide real-time information regarding the state of the cardiac tissue surrounding the catheter tip. Nevertheless, the formation and interpretation of IEGMs during the RFA procedure is complex and yet not fully understood. In this in-silico study, we propose a computational model for acute ablation lesions. Our model consists of a necrotic scar core and a border zone, describing irreversible and reversible temperature induced electrophysiological phenomena. These phenomena are modeled by varying the intra- and extracellular conductivity of the tissue as well as a regulating zone factor. The computational model is evaluated regarding its feasibility and validity. Therefore, this model was compared to an existing one and to clinical measurements of five patients undergoing RFA. The results show that the model can indeed be used to recreate IEGMs. We computed IEGMs arising from complex ablation scars, such as scars with gaps or two overlapping ellipsoid scars. For orthogonal catheter orientation, the presence of a second necrotic core in the near-field of a punctiform acute ablation lesion had minor impact on the resulting signal morphology. The presented model can serve as a base for further research on the formation and interpretation of IEGMs

Personalizing Simulations of the Human Atria : Intracardiac Measurements, Tissue Conductivities, and Cellular Electrophysiology

This work addresses major challenges of heart model personalization. Analysis techniques for clinical intracardiac electrograms determine wave direction and conduction velocity from single beats. Electrophysiological measurements are simulated to validate the models. Uncertainties in tissue conductivities impact on simulated ECGs. A minimal model of cardiac myocytes is adapted to the atria. This makes personalized cardiac models a promising technique to improve treatment of atrial arrhythmias

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

A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms

Computational simulations of cardiac electrophysiology provide detailed information on the depolarization phenomena at different spatial and temporal scales. With the development of new hardware and software, in silico experiments have gained more importance in cardiac electrophysiology research. For plane waves in healthy tissue, in vivo and in silico electrograms at the surface of the tissue demonstrate symmetric morphology and high peak-to-peak amplitude. Simulations provided insight into the factors that alter the morphology and amplitude of the electrograms. The situation is more complex in remodeled tissue with fibrotic infiltrations. Clinically, different changes including fractionation of the signal, extended duration and reduced amplitude have been described. In silico, numerous approaches have been proposed to represent the pathological changes on different spatial and functional scales. Different modeling approaches can reproduce distinct subsets of the clinically observed electrogram phenomena. This review provides an overview of how different modeling approaches to incorporate fibrotic and structural remodeling affect the electrogram and highlights open challenges to be addressed in future research

A Multiscale in Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation. A Translational Study to Guide Ablation Therapy

[ES] La fibrilación auricular es la arritmia cardíaca más común. Durante la fibrilación auricular, el sustrato auricular sufre una serie de cambios o remodelados a nivel eléctrico y estructural. La remodelación eléctrica se caracteriza por la alteración de una serie de canales iónicos, lo que cambia la morfología del potential de transmembrana conocido como potencial de acción. La remodelación estructural es un proceso complejo que involucra la interacción de varios procesos de señalización, interacción celular y cambios en la matriz extracelular. Durante la remodelación estructural, los fibroblastos que abundan en el tejido cardíaco, comienzan a diferenciarse en miofibroblastos que son los encargados de mantener la estructura de la matriz extracelular depositando colágeno. Además, la señalización paracrina de los miofibroblastos afecta a los canales iónicos de los miocitos circundantes. Se utilizaron modelos computacionales muy detallados a diferentes escalas para estudiar la remodelación estructural inducida a nivel celular y tisular. Se realizó una adaptación de un modelo de fibroblastos humanos a nivel celular para reproducir la electrofisiología de los miofibroblastos durante la fibrilación auricular. Además, se evaluó la exploración de la interacción del calcio en la electrofisiología de los miofibroblastos ajustando el canal de calcio a los datos experimentales. A nivel tisular, se estudió la infiltración de miofibroblastos para cuantificar el aumento de vulnerabilidad a una arritmia cardíaca. Los miofibroblastos cambian la dinámica de la reentrada. Una baja densidad de miofibroblastos permite la propagación a través del área fibrótica y crea puntos de salida de actividad focal y roturas de ondas dentro de esta área. Además, las composiciones de fibrosis juegan un papel clave en la alteración del patrón de propagación. La alteración del patrón de propagación afecta a los electrogramas recogidos en la superficie del tejido. La morfología del electrograma se alteró dependiendo de la disposición y composición del tejido fibrótico. Se combinaron modelos detallados de tejido cardíaco con modelos realistas de los catéteres de mapeo disponibles comercialmente para comprender las señales registradas clínicamente. Se generó un modelo de ruido a partir de señales clínicas para reproducir los artefactos de señal en el modelo. Se utilizaron electrogramas de modelos de dos dominios altamente detallados para entrenar un algoritmo de aprendizaje automático para caracterizar el sustrato fibrótico auricular. Las características que cuantifican la complejidad de las señales fueron extraídas para identificar la densidad fibrótica y la transmuralidad fibrótica. Posteriormente, se generaron mapas de fibrosis utilizando el registro del paciente como prueba de concepto. El mapa de fibrosis proporciona información sobre el sustrato fibrótico sin utilizar un valor único de corte de 0,5 milivoltios. Además, utilizando la medición del flujo de información como la entropía de transferencia combinada con gráficos dirigidos, en este estudio, se siguió la dirección de propagación del frente de onda. La transferencia de entropía con gráficos dirigidos proporciona información crucial durante la electrofisiología para comprender la dinámica de propagación de ondas durante la fibrilación auricular. En conclusión, esta tesis presenta un estudio in silico multiescala que proporciona información sobre los mediadores celulares responsables de la remodelación de la matriz extracelular y su electrofisiología. Además, proporciona una configuración realista para crear datos in silico que pueden ser usados para aplicaciones clínicas y servir de soporte al tratamiento de ablación.[CA] La fibril·lació auricular és l'arrítmia cardíaca més freqüent, en la qual el substrat auricular patix una sèrie de remodelacions elèctriques i estructurals. La remodelació de tipus elèctric es caracteritza per l'alteració d'un conjunt de canals iònics que modifica la morfologia del voltatge transmembrana, conegut com a potencial d'acció. La remodelació estructural és un fenomen complex que implica la relació entre diversos processos de senyalització, interaccions cel·lulars i canvis en la matriu extracel·lular. Durant la remodelació estructural, els abundants fibroblasts presents en el teixit cardíac comencen a diferenciar-se en miofibroblasts, els quals s'encarreguen de mantenir l'estructura de la matriu extracel·lular dipositant-hi col·lagen. A més, la senyalització paracrina dels miofibroblasts amb els miòcits circumdants també afectarà els canals iònics. Es van utilitzar models computacionals molt detallats a diferents escales per estudiar la remodelació estructural induïda a nivell tissular i cel·lular. Es va fer una adaptació a nivell cel·lular d'un model de fibroblasts humans per reproduir-hi l'electrofisiologia dels miofibroblasts durant la fibril·lació auricular. A més, l'exploració de la interacció del calci amb l'electrofisiologia dels miofibroblasts va ser avaluada mitjançant l'adequació del canal de calci a les dades experimentals. A nivell tissular es va estudiar la infiltració de miofibroblasts per tal de quantificar l'augment de vulnerabilitat que això conferia per patir una arrítmia cardíaca. Els miofibroblasts canvien la dinàmica de la reentrada, i presentar-ne una baixa densitat permet la propagació a través de la zona fibròtica, tot creant punts de sortida d'activitat focal i trencaments d'ones dins d'aquesta àrea. A més, les composicions de fibrosi tenen un paper clau en l'alteració del patró de propagació, afectant els electrogrames recollits en la superfície del teixit. La morfologia dels electrogrames es va veure alterada en funció de la disposició i la composició del teixit fibròtic. Per comprendre els senyals clínicament registrats es van combinar models detallats de teixits cardíacs amb models realistes dels catèters de cartografia disponibles comercialment. Es va generar un model de soroll a partir de senyals clínics per reproduir-hi els artefactes de senyal. Es van utilitzar electrogrames de models de bidominis molt detallats per entrenar un algoritme d'aprenentatge automàtic destinat a caracteritzar el substrat fibròtic auricular. Les característiques que quantifiquen la complexitat dels senyals van ser extretes per identificar la densitat i transmuralitat fibròtica. Posteriorment, es van generar mapes de fibrosi mitjançant la gravació del pacient com a prova de concepte. El mapa de fibrosi proporciona informació sobre el substrat fibròtic sense utilitzar un sol valor de tensió de tall de 0,5 mV. A més, utilitzant la mesura del flux d'informació com l'entropia de transferència combinada amb gràfics dirigits, en aquest estudi es va fer un seguiment de la direcció de propagació de l'ona. L'entropia de transferència amb gràfics dirigits proporciona informació crucial durant l'electrofisiologia per entendre la dinàmica de propagació d'ones durant la fibril·lació auricular. En conclusió, aquesta tesi presenta un estudi multi-escala in silico que proporciona informació sobre els mediadors cel·lulars responsables de la remodelació de la matriu extracel·lular i la seva electrofisiologia. A més, proporciona una configuració realista per crear dades in silico que es poden traduir a aplicacions clíniques que puguen donar suport al tractament de l'ablació.[EN] Atrial fibrillation is the most common cardiac arrhythmia. During atrial fibrillation, the atrial substrate undergoes a series of electrical and structural remodeling. The electrical remodeling is characterized by the alteration of specific ionic channels, which changes the morphology of the transmembrane voltage known as action potential. Structural remodeling is a complex process involving the interaction of several signalling pathways, cellular interaction, and changes in the extracellular matrix. During structural remodeling, fibroblasts, abundant in the cardiac tissue, start to differentiate into myofibroblasts, which are responsible for maintaining the extracellular matrix structure by depositing collagen. Additionally, myofibroblasts paracrine signalling with surrounding myocytes will also affect ionic channels. Highly detailed computational models at different scales were used to study the effect of structural remodeling induced at the cellular and tissue levels.At the cellular level, a human fibroblast model was adapted to reproduce the myofibroblast electrophsyiology during atrial fibrillation. Additionally, the calcium handling in myofibroblast electrophysiology was assessed by fitting calcium ion channel to experimental data. At the tissue level, myofibroblasts infiltration was studied to quantify the increase of vulnerability to cardiac arrhythmia. Myofibroblasts alter the dynamics of reentry. A low density of myofibroblasts allows the propagation through the fibrotic area and creates focal activity exit points and wave breaks inside this area. Moreover, fibrosis composition plays a key role in the alteration of the propagation pattern. The alteration of the propagation pattern affects the electrograms computed at the surface of the tissue. Electrogram morphology was altered depending on the arrangement and composition of the fibrotic tissue. Detailed cardiac tissue models were combined with realistic models of the commercially available mapping catheters to understand the clinically recorded signals. A noise model from clinical signals was generated to reproduce the signal artifacts in the model. Electrograms from highly detailed bidomain models were used to train a machine learning algorithm to characterize the atrial fibrotic substrate. Features that quantify the complexity of the signals were extracted to identify fibrotic density and fibrotic transmurality. Subsequently, fibrosis maps were generated using patient recordings as a proof of concept. Fibrosis map provides information about the fibrotic substrate without using a single cut-off voltage value of 0.5 mV. Furthermore, in this study, using information theory measurements such as transfer entropy combined with directed graphs, the wave propagation direction was tracked. Transfer entropy with directed graphs provides crucial information during electrophysiology to understand wave propagation dynamics during atrial fibrillation. In conclusion, this thesis presents a multiscale in silico study atrial fibrillation mechanisms providing insight into the cellular mediators responsible for the extracellular matrix remodeling and its electrophysiology. Additionally, it provides a realistic setup to create in silico data that can be translated to clinical applications that could support ablation treatment.Sánchez Arciniegas, JP. (2021). A Multiscale in Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation. A Translational Study to Guide Ablation Therapy [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171456TESI

Separating atrial near fields and atrial far fields in simulated intra-Atrial electrograms

The detailed characterization of complex forms of atrial flutter relies on the correct interpretation of intra-atrial electrograms. For this, the near fieldcomponents, which represent the local electrical activity, are decisive. However, far field components arising from distant electrical sources in the atria can obscure the diagnosis.We developed a method to separate and characterize atrial near field and atrial far field components from bipolar intra-atrial electrograms. First, a set of bipolar electrograms was created by simulating different propagation scenarios representing common clinical depolarizationpatterns. Second, near and far fields were detected as active segments usinga non-linear energy operator-based approach. Third, the maximum slope and the spectralpower were extracted as features for all active segments. Active segments were grouped accounting for both the timing and the location of their occurrence. In a last step, the active segments were classified in near and far fields by comparing their feature values to a threshold.All active segments were detected correctly. On average, near fields showed 15.1x larger maximum slopes and 40.4x larger spectral powers above 100 Hz than far fields. For 135 active segments detected in 72 bipolar electrograms, 5.2% and 6.7% were misclassified using the maximum slope and the spectral power, respectively. All active segments were classified correctly if only one near field segment was assumed to occur per electrogram.The separation of atrial near andatrial far fields was successfully developed and applied to in silico electrograms.Theseinvestigations providea promising basis fora future clinical study to ultimatelyfacilitatethe precise clinical diagnosis of atrial flutter