Nonlinear analysis of human atrial flutter and fibrillation using surface electrocardiogram

Kao, Tsair; Su, Yi; Tso, Han-Wen; Lin, Yajuan; Chen, S.A.; Tai, Ching Tai

doi:10.1109/cic.2004.1442968

Cited by 5 publications

(11 citation statements)

References 6 publications

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“…However, multivariate prediction involving other parameters may improve the performance when larger databases are analyzed. Various complexity measures have previously been applied to characterize organization of the atrial activation time series which are obtained from atrial electrograms [13,20]. The method proposed in this paper differs from these complexity measures in that its purpose is to characterize atrial waveforms using the surface ECG signals which are non-invasive and more convenient to obtain.…”

Section: B Performance Of the Methodsmentioning

confidence: 99%

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Predicting Termination of Atrial Fibrillation Based on Shape Analysis in Reconstructed State Space

Sun

Wang

2007

2007 IEEE/ICME International Conference on Complex Medical Engineering

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It is important to recognize and characterize conditions under which atrial fibrillation (AF) is likely to terminate spontaneously or be sustained. This leads to not only better understanding of onset and termination mechanisms of the arrhythmia but also improved treatment of sustained AF. In this paper, a novel methodology is proposed to characterize AF from a nonlinear point of view to predict its spontaneous termination. The state space of the electrocardiography (ECG) signal is firstly reconstructed and points on a given Poincare surface are studied. A Poincare surface is a hyperplane intersecting the coordinate axis of interest at a given position in the reconstructed state space. Then a regularity index, p is proposed to characterize the geometric structures of the points on Poincare surface based on the shape analysis. It is defined by calculating the relative number of similar pairs of points on the Poincare surface. Finally, a threshold is calculated as the value of p which minimizes the prediction error for the training data set to classify non-terminating and terminating AF.Here an AF database which includes a training set with known termination properties and two testing sets of Holter ECG recordings is studied in the experiments. Results show that with the feature proposed in this study, 90% of the testing set is correctly classified. It demonstrates that the algorithm has the ability to predict spontaneous termination of AF from Holter ECG recordings effectively.

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Section: B Performance Of the Methodsmentioning

confidence: 99%

“…The exponential decay is a recently presented parameter which characterizes the fibrillatory waves by the magnitudes of the fundamental frequency and its harmonics [8]. Cross-correlation techniques [9], linear prediction [10], and nonlinear analysis [11][12][13][14] are also proposed.…”

Section: Introductionmentioning

confidence: 99%

Predicting Termination of Atrial Fibrillation Based on Shape Analysis in Reconstructed State Space

Sun

Wang

2007

2007 IEEE/ICME International Conference on Complex Medical Engineering

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“…15 All of those methods are limited by the sensitivity to experimental noise, low robustness with shorter durations of time series, and the sensitivity to initial parameter selection. [25][26][27][28][29][30][31] In addition, the Grassberger-Procaccia method and Lyapunov exponents 14,15 could falsely classify highly correlated stochastic time series as deterministic. 25,32 The critical strength of our work is that we used a combination of the BP symbolization with the Amig o methodology and the causal entropycomplexity plane, both of which are robust against all of those limitations.…”

Section: B Determinism Of Atrial Fibrillationmentioning

confidence: 99%

“…62,63 In addition, human AF had higher values of several nonlinear parameters such as Lyapunov exponent, Kolmogorov entropy, and Lempel-Ziv complexity compared to human typical atrial flutter. 15 However, an additional strength of our work is that it evaluates real-life, clinically acquired electrophysiology recording that directly capture AF dynamics.…”

Section: B Determinism Of Atrial Fibrillationmentioning

confidence: 99%

“…It remains controversial whether the human AF dynamics result from a deterministic or a stochastic process. [13][14][15][16] Elucidating whether the disorganized dynamics observed in human AF is the result of a deterministic or a stochastic process is essential for a proper physical description of AF. Identifying determinism in AF time series is critically important for understanding the mechanism of modeling and predicting AF.…”

Section: Introductionmentioning

confidence: 99%

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Is human atrial fibrillation stochastic or deterministic?—Insights from missing ordinal patterns and causal entropy-complexity plane analysis

Aronis

Berger

Calkins

et al. 2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The mechanism of atrial fibrillation (AF) maintenance in humans is yet to be determined. It remains controversial whether cardiac fibrillatory dynamics are the result of a deterministic or a stochastic process. Traditional methods to differentiate deterministic from stochastic processes have several limitations and are not reliably applied to short and noisy data obtained during clinical studies. The appearance of missing ordinal patterns (MOPs) using the Bandt-Pompe (BP) symbolization is indicative of deterministic dynamics and is robust to brief time series and experimental noise. Our aim was to evaluate whether human AF dynamics is the result of a stochastic or a deterministic process. We used 38 intracardiac atrial electrograms during AF from the coronary sinus of 10 patients undergoing catheter ablation of AF. We extracted the intervals between consecutive atrial depolarizations (AA interval) and converted the AA interval time series to their BP symbolic representation (embedding dimension 5, time delay 1). We generated 40 iterative amplitude-adjusted, Fourier-transform (IAAFT) surrogate data for each of the AA time series. IAAFT surrogates have the same frequency spectrum, autocorrelation, and probability distribution with the original time series. Using the BP symbolization, we compared the number of MOPs and the rate of MOP decay in the first 1000 timepoints of the original time series with that of the surrogate data. We calculated permutation entropy and permutation statistical complexity and represented each time series on the causal entropy-complexity plane. We demonstrated that (a) the number of MOPs in human AF is significantly higher compared to the surrogate data (2.7 ± 1.18 vs. 0.39 ± 0.28, p < 0.001); (b) the median rate of MOP decay in human AF was significantly lower compared with the surrogate data (6.58 × 10 vs. 7.79 × 10, p < 0.001); and (c) 81.6% of the individual recordings had a rate of decay lower than the 95% confidence intervals of their corresponding surrogates. On the causal entropy-complexity plane, human AF lay on the deterministic part of the plane that was located above the trajectory of fractional Brownian motion with different Hurst exponents on the plane. This analysis demonstrates that human AF dynamics does not arise from a rescaled linear stochastic process or a fractional noise, but either a deterministic or a nonlinear stochastic process. Our results justify the development and application of mathematical analysis and modeling tools to enable predictive control of human AF.

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Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot

Sun

Wang

2008

Medical Engineering & Physics

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Nonlinear analysis of human atrial flutter and fibrillation using surface electrocardiogram

Cited by 5 publications

References 6 publications

Predicting Termination of Atrial Fibrillation Based on Shape Analysis in Reconstructed State Space

Predicting Termination of Atrial Fibrillation Based on Shape Analysis in Reconstructed State Space

Is human atrial fibrillation stochastic or deterministic?—Insights from missing ordinal patterns and causal entropy-complexity plane analysis

Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot

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