Optimal parameters study for sample entropy-based atrial fibrillation organization analysis

Alcaraz, Raúl; Abásolo, Daniel; Hornero, Roberto; Rieta, J.J.

doi:10.1016/j.cmpb.2010.02.009

Cited by 93 publications

(83 citation statements)

References 31 publications

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“…For sampling frequencies lower than 128 Hz, LZ complexity does not reflect significant differences between the terminating and non-terminating patients. This also happened when the same database was analysed with SampEn [32] and suggests that the sampling frequency should be at least 256 Hz in order to accurately estimate AF organization. Nevertheless, an important difference between this study and [32] is that the diagnostic accuracy of LZ complexity increases with the sampling frequency, irrespective of the use of a 2 or 3 symbol conversion and the mean or median as threshold.…”

Section: Discussionmentioning

confidence: 99%

“…In a recent study the prediction of spontaneous AF termination with SampEn was achieved with a diagnostic accuracy of 96% [32]. In this pilot study we wanted to evaluate if that prediction could be improved with a different non-linear analysis technique, namely LZ complexity, whilst at the same time testing the different parameters that play a role in the computation of this metric.…”

Section: Discussionmentioning

confidence: 99%

“…Ideally, it would be better to digitise the recordings at different sampling rates and then calculate its LZ complexity. Nonetheless, it has been shown that the cubic splines interpolation method of AA did not have a significant impact on results obtained with SampEn [32]. Secondly, the accuracy values were estimated with the whole subsets, something that could cause problems like over-fitting and bias.…”

Section: Discussionmentioning

confidence: 99%

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Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

Abásolo¹,

Alcaraz²,

Rieta³

et al. 2011

Biomedical Engineering

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The Lempel-Ziv (LZ) complexity is a non-linear time series analysis metric that reflects the arising rate of new patterns along with the sequence. Thus, it captures its temporal sequence and, quite conveniently, it can be computed with short data segments. In the present work, a detailed analysis on LZ complexity is presented within the context of atrial fibrillation (AF) organization estimation. As the analysed time series depend on the original sampling rate (fs), we evaluated the relationship between LZ complexity and fs. Furthermore, different implementations of LZ complexity were tested. Our results show the usefulness of LZ complexity to estimate AF organization and suggest that the signals from a terminating paroxysmal AF group are more organized (i.e. less complex) than those from the non-terminating paroxysmal AF group. However, the diagnostic accuracy was not as high as that obtained with sample entropy (SampEn), another non-linear metric, with the same database in a previous study (92% vs. 96%). Nevertheless, the LZ complexity analysis of AF organization with sampling frequencies higher than 2048 Hz, or even its combination with SampEn or other non-linear metrics, might improve the prediction of spontaneous AF termination.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

Abásolo¹,

Alcaraz²,

Rieta³

et al. 2011

Biomedical Engineering

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“…Nonetheless, many authors use the typically recommended values of m = 1 or 2 and r between 0.05-and 0.25-times the standard deviation of the data [38]. However, several works have proven that these values are inappropriate for some applications [44,45]. Indeed, recent studies trying to optimize (m, r) for specific scenarios can be found in the literature [46].…”

Section: Study Of Optimal Parameters To Compute Entropy Measuresmentioning

confidence: 99%

“…However, in the context of shock outcome prediction, the VF-amplitude conveys relevant information on the metabolic state of the myocardium [19], and therefore, this normalization has not been used in this study. A systematic and thorough analysis on the optimal (m, r) values for ApEn, SampEn and FuzzEn was conducted, in line with some of our previous works [45]. Entropy measures were computed using a 3 × 20 matrix of combinations of m = 1, 2, 3 and r = 5, 10, 15, ..., 100 µV.…”

Section: Study Of Optimal Parameters To Compute Entropy Measuresmentioning

confidence: 99%

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Chicote

Irusta

Alcaraz

et al. 2016

Entropy

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Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 µV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.

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The Sample Entropy of Two Spatial Links

Marghitu

2022

Springer Proceedings in Physics

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Optimal parameters study for sample entropy-based atrial fibrillation organization analysis

Cited by 93 publications

References 31 publications

Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

The Sample Entropy of Two Spatial Links

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