Modeling Framework for the Generation of Synthetic RR Series during Atrial Arrhythmias

Masè, Michela; Marsili, I. A.; Nollo, Giandomenico; Ravelli, Flavia

doi:10.1109/embc.2019.8857842

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The fit residual indicates whether this is a suitable assumption, and the data may be divided into separate wavefronts as a pre-processing step. Both polynomial surface fitting [33] and radial basis function methodologies [34] are suitable for multiple wavefronts and any arrangement of points, but have larger data requirements and may over interpolate data. Kay and Gray developed a method for estimating wavefront curvature from isopotential lines, which they demonstrate is accurate for high resolution optical mapping data [35].…”

Section: Discussionmentioning

confidence: 99%

A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction

Roney

Whitaker

Sim

et al. 2019

Computers in Biology and Medicine

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Background Cardiac conduction properties exhibit large variability, and affect patient-specific arrhythmia mechanisms. However, it is challenging to clinically measure conduction velocity (CV), anisotropy and fibre direction. Our aim is to develop a technique to estimate conduction anisotropy and fibre direction from clinically available electrical recordings. Methods We developed and validated automated algorithms for estimating cardiac CV anisotropy, from any distribution of recording locations on the atrial surface. The first algorithm is for elliptical wavefront fitting to a single activation map (method 1), which works well close to the pacing location, but decreases in accuracy further from the pacing location (due to spatial heterogeneity in the conductivity and fibre fields). As such, we developed a second methodology for measuring local conduction anisotropy, using data from two or three activation maps (method 2: ellipse fitting to wavefront propagation velocity vectors from multiple activation maps). Results Ellipse fitting to CV vectors from two activation maps (method 2) leads to an improved estimation of longitudinal and transverse CV compared to method 1, but fibre direction estimation is still relatively poor. Using three activation maps with method 2 provides accurate estimation, with approximately of atrial fibres estimated within . We applied the technique to clinical activation maps to demonstrate the presence of heterogeneous conduction anisotropy, and then tested the effects of this conduction anisotropy on predicted arrhythmia dynamics using computational simulation. Conclusions We have developed novel algorithms for calculating CV and measuring the direction dependency of atrial activation to estimate atrial fibre direction, without the need for specialised pacing protocols, using clinically available electrical recordings.

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Section: Discussionmentioning

confidence: 99%

A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction

Roney

Whitaker

Sim

et al. 2019

Computers in Biology and Medicine

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“…Instead of using the simple two-state, continuoustime Markov chain for rhythm switching, the proposed model can be used to simulate more realistic paroxysmal AF patterns. The three-state, continuous-time Markov chain used to model sinus rhythm, atrial flutter, and AF [40] may as well be replaced, but then probably by a trivariate Hawkes model.…”

Section: Discussionmentioning

confidence: 99%

Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation

Henriksson

Martín-Yebra

Butkuvienė

et al. 2021

IEEE Trans. Biomed. Eng.

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The present study proposes a modelbased, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice. Methods: History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data. Results: Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden. Conclusion:Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.

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Challenging the Limitations of Atrial Fibrillation Detection in the Presence of Other Cardiac Arrythmias

Genzoni

Braun

Zaen

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Modeling Framework for the Generation of Synthetic RR Series during Atrial Arrhythmias

Cited by 3 publications

References 18 publications

A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction

A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction

Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation

Challenging the Limitations of Atrial Fibrillation Detection in the Presence of Other Cardiac Arrythmias

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