Non-invasive estimation of left atrial dominant frequency in atrial fibrillation from different electrode sites: Insight from body surface potential mapping

© 2014, CardioFront LLC. All rights reserved. The dominant driving sources of atrial fibrillation are often found in the left atrium, but the expression of left atrial activation on the body surface is poorly understood. Using body surface potential mapping and simultaneous invasive measurements of left atrial activation our aim was to describe the expression of the left atrial dominant fibrillation frequency across the body surface. 20 patients in atrial fibrillation were studied. The spatial distributions of the dominant atrial fibrillation frequency across anterior and posterior sites on the body surface were quantified. Their relationship with invasive left atrial dominant fibrillation frequency was assessed by linear regression analysis, and the coefficient of determination was calculated for each body surface site. The correlation between intracardiac and body surface dominant frequency was significantly higher with posterior compared with anterior sites (coefficient of determination 67±8% vs 48±2%,

Augmented Two-Channel Arrhythmia Detection: An Efficient Diagnostic Method for Implantable Devices

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75624/1/j.1540-8159.1996.tb03164.x.pd

Analysis of Atrial Electrograms

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia

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

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

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF

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

The atrial substrate undergoes electrical and structural remodeling during atrial fibrillation. Detailed multiscale models were used to study the effect of structural remodeling induced at the cellular and tissue levels. Simulated electrograms were used to train a machine-learning algorithm to characterize the substrate. Also, wave propagation direction was tracked from unannotated electrograms. In conclusion, in silico experiments provide insight into electrograms\u27 information of the substrate

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

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