Methods for analyzing signal characteristics of stable and unstable rotors in a realistic heart model

Rottmann, Markus; Unger, Laura; Kaltenbacher, Wenzel; Seemann, Gunnar; Loewe, Axel; Krueger, Martin W.; Jadidi, Amir; Arentz, Thomas; Dössel, Olaf

doi:10.1109/cic.2015.7410953

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…AF termination sites displayed repetitive CLC >70% on the Lasso or a 20-pole circular mapping catheter in 92% of cases and rapid focal activity in 8% of AF termination sites. Moreover, localized rotational activity was associated with the typical "line pattern" [5] on the circular mapping catheters, compare figure 2B.…”

Section: Rotational Activity Was Detected By Clc > 70% In Clinical Mamentioning

confidence: 79%

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Impact of Three Dimensional Atrial Fibrosis on Development and Stability of Rotational Activity in Atrial Fibrillation: A 3D Simulation and Clinical High:density Mapping Study in Peristent Atrial Fibrillation

Rottmann

Markstein

Arslan

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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The arrhythmogenic mechanisms of atrial fibrillation (AF) are still not well understood. Increased atrial fibrosis is a structural hallmark in patients with persistent AF. We assessed the electrogram signature rotational activity and their spatial relationship to low voltage areas in patients with persistent AF. Computer simulations implicating 3-dimensional atrial tissue with different amount of atrial fibrosis were used to assess development and stability of rotational activities during AF. Rotor anchoring occurred at the borderzone between fibrosis and healthy atrial tissue with 12 consecutive rotations prior to rotor extinction. Rotational activity in fibrotic tissue resulted in fractionated signals and were overlapped with large negative electrograms in unipolar recording mode from neighboring healthy tissue -impressing as a focal source. Necessary conditions for development and stability of rotational activities around fibrosis were on the one hand a minimum size of atrial fibrosis area equal or larger than 10mm x 10mm and on the other hand the degree of atrial fibrosis of 40%. Clinical data showed that AF termination sites were located within low voltage areas (displaying <0,5mV in AF on the multielectrode mapping catheter) in 80% and at their borderzones in 20% of cases.

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Section: Rotational Activity Was Detected By Clc > 70% In Clinical Mamentioning

confidence: 79%

“…Within one cycle length the parameter cycle length coverage (CLC) [5] was calculated with equation 1.…”

Section: Cycle Length Coveragementioning

confidence: 99%

Impact of Three Dimensional Atrial Fibrosis on Development and Stability of Rotational Activity in Atrial Fibrillation: A 3D Simulation and Clinical High:density Mapping Study in Peristent Atrial Fibrillation

Rottmann

Markstein

Arslan

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…The use of patient-specific 3D models can give more insight into the mechanism of AF (Zahid et al, 2016). Those simulations are used for the analysis of the mechanisms for AF initiation (Virag et al, 2002; Chang and Trayanova, 2016; Alcaine et al, 2017), AF stability (Rottmann et al, 2015) or the rotor drifting toward PVs (Calvo et al, 2014) and their anchoring in obstacles, such as fibrotic regions (Morgan et al, 2016; Nguyen et al, 2016; Deng et al, 2017). The detection of rotors is also studied using 3D simulations of human atria (Ugarte et al, 2014; Berenfeld, 2016; Martinez et al, 2016) to investigate the effectiveness of an ablation procedure (Dang et al, 2005; Hwang et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

et al. 2019

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The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

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“…Complexity measurements obtained from simulated intracardiac signals help understand the electrophysiology and the fibrotic tissue structural characteristics. Other studies showed that Shannon entropy and fractal dimensions help to localize the core of rotational activity 30,31 . Cirugeda-Roldan et al showed that sample entropy is a robust feature to classify complex fractionated electrograms 30 .…”

Section: Discussionmentioning

confidence: 99%

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

Sánchez

Luongo

Nothstein

et al. 2021

Preprint

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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.

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Methods for analyzing signal characteristics of stable and unstable rotors in a realistic heart model

Cited by 8 publications

References 12 publications

Impact of Three Dimensional Atrial Fibrosis on Development and Stability of Rotational Activity in Atrial Fibrillation: A 3D Simulation and Clinical High:density Mapping Study in Peristent Atrial Fibrillation

Impact of Three Dimensional Atrial Fibrosis on Development and Stability of Rotational Activity in Atrial Fibrillation: A 3D Simulation and Clinical High:density Mapping Study in Peristent Atrial Fibrillation

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

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

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