Abstract:

Background: Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity. Methods and Results: The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude. Conclusion: The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.

Abstract:

BACKGROUND AND OBJECTIVE: Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. METHODS AND RESULTS: openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. CONCLUSION: As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.

Abstract:

AIMS: The treatment of atrial fibrillation beyond pulmonary vein isolation has remained an unsolved challenge. Targeting regions identified by different substrate mapping approaches for ablation resulted in ambiguous outcomes. With the effective refractory period being a fundamental prerequisite for the maintenance of fibrillatory conduction, this study aims at estimating the effective refractory period with clinically available measurements. METHODS AND RESULTS: A set of 240 simulations in a spherical model of the left atrium with varying model initialization, combination of cellular refractory properties, and size of a region of lowered effective refractory period was implemented to analyse the capabilities and limitations of cycle length mapping. The minimum observed cycle length and the 25% quantile were compared to the underlying effective refractory period. The density of phase singularities was used as a measure for the complexity of the excitation pattern. Finally, we employed the method in a clinical test of concept including five patients. Areas of lowered effective refractory period could be distinguished from their surroundings in simulated scenarios with successfully induced multi-wavelet re-entry. Larger areas and higher gradients in effective refractory period as well as complex activation patterns favour the method. The 25% quantile of cycle lengths in patients with persistent atrial fibrillation was found to range from 85 to 190 ms. CONCLUSION: Cycle length mapping is capable of highlighting regions of pathologic refractory properties. In combination with complementary substrate mapping approaches, the method fosters confidence to enhance the treatment of atrial fibrillation beyond pulmonary vein isolation particularly in patients with complex activation patterns.

Abstract:

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 and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from 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.

Abstract:

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice. There is increasing evidence from a mechanistic point of view that pathological atrial substrate (fibrosis) plays a central role in the maintenance and perpetuation of AF. AF is treated by ablation of fibrotic substrate. However, detection of such substrate is an ongoing challenge as demonstrated by poor clinical ablation outcomes. Therefore, the main topic of this work is the characterization of atrial substrate. Determining signal characteristics at fibrotic substrate sites could make detection and subsequently ablation of such sites easier in future. Additionally, understanding of how these sites uphold AF can increase positive outcome of AF ablation procedures. Lastly, restitution information could be a further tool of substrate characterization that could help with distinction of pathological and non-pathological sites and therefore further improve ablation outcome. In this thesis two approaches for substrate characterization are presented. Firstly, substrate was characterized by proposing electrogram characteristics that defined sites maintaining AF, which after ablation terminated AF. This study was performed on 21 patients in whom low-voltage-guided ablation after pulmonary vein isolation terminated clinical persistent AF. Successful termination sites of AF displayed distinct electrogram patterns with short local cycle lengths that included fractionated and low-voltage potentials that were locally highly consistent and covered a majority of the local AF cycle length. Most of these areas also exhibited pathologic delayed atrial late potentials and fractionated electrograms in sinus rhythm. Secondly, restitution information of local amplitude and local conduction velocity (CV) was acquired and used to infer information on the underlying substrate. Restitution data was gained from 22 AF patients from two clinics by using a S1S2 protocol between pacing intervals of 180 ms to 500 ms. To obtain restitution data from the patient group, an automated algorithm capable of reading, segmenting, and analyzing large amounts of stimulation protocol data had to be developed. This algorithm was developed as part of this work and is called CVAR-Seg. The CVAR-Seg algorithm provided noise-robust signal segmentation up until noise levels far exceeding expected clinical noise levels. CVAR-Seg was released as open source to the community and due to its modular arrangement, enables easy replacement of each of the single process steps by alternative methods according to the user’s needs. Additionally, a novel method called inverse double ellipse method was established to determine local CV within the scope of this study. This inverse double ellipse method estimated CV, fiber orientation and anisotropy factor from any electrode arrangement and reproduced in-silico CV, fiber orientation and CV anisotropy more accurately and more robust than the current state-of-the-art method. Furthermore, the method proved to be real-time capable and thus a valid consideration to implement in clinical electrophysiology systems. This would enable instantaneous localized measurement of atrial substrate information, gaining a CV map, an anisotropy ratio map, and a fiber map simultaneously during one mapping procedure. Restitution information of the patient cohort was evaluated using the CVAR-Seg pipeline and the inverse double ellipse method to acquire amplitude and CV restitution curves. Restitution curves were fitted using a mono-exponential function. The fit parameters representing the restitution curves were used to discern differences in restitution properties between pathological and non-pathological substrate. The result was that clinically defined low voltage (LV) zones were characterized by a reduced amplitude asymptote and a steep decay with increased pacing rate, whereas CV curves showed a reduced CV asymptote and a high range of decay values. Moreover, restitution differences within the atrial body at the posterior and anterior wall were compared, since literature reports revealed inconclusive results. In this work, the posterior atrial wall was found to contain amplitude and CV restitution curves with higher asymptote and more moderate curvature than the anterior atrial wall. To move beyond the empirically described manually chosen threshold used currently, the parameter space spanned by the fit parameters of the amplitude and CV restitution curves was searched for naturally occurring clusters. While clusters were present, their inadequate separation from each other indicated a continuous progression of the amplitude curves as well as the CV curves with the level of the substrate pathology. Lastly, an easier and faster method to acquire restitution data was proposed that is based on acquisition of the maximum slope and provides comparable information content to a full restitution curve. This work presents two novel methods, the CVAR-Seg algorithm and the inverse double ellipse fit that expedite and refine evaluation of S1S2 protocols and estimation of local CV. Furthermore, this work defines characteristics of pathological tissue that help identify sources of arrhythmia. Thus, this work may help to improve the therapy of AF in the future.