Recurrence Analysis of Cardiac Restitution in Human Ventricle

Arce, Humberto; Fuentes, Ayari; González, Gabriel Alcántar

doi:10.1007/978-3-319-29922-8_9

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…The dynamics of cardiac signals in the presence of arrhythmias have been extensively investigated in the context of recurrence analysis, especially when considering the main advantages of recurrence plots (RPs) and recurrence quantification analysis (RQA) for characterizing short time series, phase transitions, nonstationarity and unveiling nonlinear underlying phenomena in general [1][2][3][4][5][6][7]. The possibility of quantifying i) signal regularity, laminarity and determinism; ii) nonlinear topological invariants -e.g., correlation dimension and Kolmogorov-Sinai entropy [8,9] -iii) information-theoretic measures -e.g., generalized entropies [8] -and; iv) mutual information [3,10,11], outlines the RQA properties as a singular framework for cardiac analysis in wide sense over more traditional methods [2,4,7,12,13].…”

Section: Introductionmentioning

confidence: 99%

Characterization of human persistent atrial fibrillation electrograms using recurrence quantification analysis

Almeida

Schlindwein

Salinet

et al. 2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Atrial fibrillation (AF) is regarded as a complex arrhythmia, with one or more co-existing mechanisms, resulting in an intricate structure of atrial activations. Fractionated atrial electrograms (AEGs) were thought to represent arrhythmogenic tissue and hence have been suggested as targets for radiofrequency ablation. However, current methods for ablation target identification have resulted in suboptimal outcomes for persistent AF (persAF) treatment, possibly due to the complex spatiotemporal dynamics of these mechanisms. In the present work, we sought to characterize the dynamics of atrial tissue activations from AEGs collected during persAF using recurrence plots (RPs) and recurrence quantification analysis (RQA). 797 bipolar AEGs were collected from 18 persAF patients undergoing pulmonary vein isolation (PVI). Automated AEG classification (normal vs. fractionated) was performed using the CARTO criteria (Biosense Webster). For each AEG, RPs were evaluated in a phase space estimated following Takens' theorem. Seven RQA variables were obtained from the RPs: recurrence rate; determinism; average diagonal line length; Shannon entropy of diagonal length distribution; laminarity; trapping time; and Shannon entropy of vertical length distribution. The results show that the RQA variables were significantly affected by PVI, and that the variables were effective in discriminating normal vs. fractionated AEGs. Additionally, diagonal structures associated with deterministic behavior were still present in the RPs from fractionated AEGs, leading to a high residual determinism, which could be related to unstable periodic orbits and suggesting a possible chaotic behavior. Therefore, these results contribute to a nonlinear perspective of the spatiotemporal dynamics of persAF.

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Section: Introductionmentioning

confidence: 99%

Characterization of human persistent atrial fibrillation electrograms using recurrence quantification analysis

Almeida

Schlindwein

Salinet

et al. 2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…For instance, the work by Trulla et al highlighted the promising applications of RQA on biomedical signals [17], describing the advantages of using RQA on investigating nonstationary and short-time cardiac datasets [18], [19]. Since then, RQA has been extensively used for characterizing the dynamics of heart rate variability [20], [21], cardiac restitution [22], or even combined with machine learning techniques for sudden cardiac death stratification [23] and ECG-based arrhythmia classification [24], [25], among other applications. RQA has been used to specifically characterize the dynamics of intracardiac signals during cardiac disorders [26], [27], [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational Study

Luongo

Schuler

Luik

et al. 2021

IEEE Trans. Biomed. Eng.

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Objective: Atrial flutter (AFl) is a common arrhythmia that can be categorized according to different self-sustained electrophysiological mechanisms. The non-invasive discrimination of such mechanisms would greatly benefit ablative methods for AFl therapy as the driving mechanisms would be described prior to the invasive procedure, helping to guide ablation. In the present work, we sought to implement recurrence quantification analysis (RQA) on 12-lead ECG signals from a computational framework to discriminate different electrophysiological mechanisms sustaining AFl. Methods: 20 different AFl mechanisms were generated in 8 atrial models and were propagated into 8 torso models via forward solution, resulting in 1,256 sets of 12lead ECG signals. Principal component analysis was applied on the 12-lead ECGs, and six RQA-based features were extracted from the most significant principal component scores in two different approaches: individual component RQA and spatial reduced RQA. Results: In both approaches, RQA-based features were significantly sensitive to the dynamic structures underlying different AFl mechanisms. Hit rate as high as 67.7% was achieved when discriminating the 20 AFl mechanisms. RQA-based features estimated for a clinical sample suggested high agreement with the results found in the computational framework. Conclusion: RQA has been shown an effective method to distinguish different AFl electrophysiological mechanisms in a non-invasive computational framework. A clinical 12-lead ECG used as proof of concept showed the value of both the simulations and the methods. Significance: The non-invasive discrimination of AFl mechanisms helps to delineate the ablation strategy, reducing time and resources required to conduct invasive cardiac mapping and ablation procedures.

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“…The technique of recurrence plots (RPs) [18,19] has been applied for the visualization and analysis of nonlinear experimental data in many different fields, from biological sciences [20][21][22] to complex systems [23][24][25]. This tool was first introduced in the context of dynamical systems to visualize the recurrence of trajectories in the phase space [19].…”

Section: Introductionmentioning

confidence: 99%

Time Recurrence Analysis of a Near Singular Billiard

Baroni

Carvalho

Castaldi

et al. 2019

MCA

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Billiards exhibit rich dynamical behavior, typical of Hamiltonian systems. In the present study, we investigate the classical dynamics of particles in the eccentric annular billiard, which has a mixed phase space, in the limit that the scatterer is point-like. We call this configuration the near singular, in which a single initial condition (IC) densely fills the phase space with straight lines. To characterize the orbits, two techniques were applied: (i) Finite-time Lyapunov exponent (FTLE) and (ii) time recurrence. The largest Lyapunov exponent λ was calculated using the FTLE method, which for conservative systems, λ > 0 indicates chaotic behavior and λ = 0 indicates regularity. The recurrence of orbits in the phase space was investigated through recurrence plots. Chaotic orbits show many different return times and, according to Slater’s theorem, quasi-periodic orbits have at most three different return times, the bigger one being the sum of the other two. We show that during the transition to the near singular limit, a typical orbit in the billiard exhibits a sharp drop in the value of λ, suggesting some change in the dynamical behavior of the system. Many different recurrence times are observed in the near singular limit, also indicating that the orbit is chaotic. The patterns in the recurrence plot reveal that this chaotic orbit is composed of quasi-periodic segments. We also conclude that reducing the magnitude of the nonlinear part of the system did not prevent chaotic behavior.

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Recurrence Analysis of Cardiac Restitution in Human Ventricle

Cited by 5 publications

References 32 publications

Characterization of human persistent atrial fibrillation electrograms using recurrence quantification analysis

Characterization of human persistent atrial fibrillation electrograms using recurrence quantification analysis

Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational Study

Time Recurrence Analysis of a Near Singular Billiard

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