Coarse-Grained Multifractality Analysis Based on Structure Function Measurements to Discriminate Healthy from Distressed Foetuses

Oudjemia, Souad; Zaylaa, Amira; Haddab, Salah; Girault, Jean‐Marc

doi:10.1155/2013/152828

Cited by 5 publications

(8 citation statements)

References 27 publications

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“…DET remained greater for healthy fetuses compared to distressed fetuses. The behavior of H-FHRs reported in this work was in accordance with the results reported in [3,4,9].…”

Section: Discussionsupporting

confidence: 91%

See 1 more Smart Citation

Advanced Discrimination between Healthy and Intrauterine Growth Restricted Fetuses by Unbiased Recurrence Plots

Zaylaa¹,

Charara²,

Girault³

2015

Adv Tech Biol Med

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“…DET remained greater for healthy fetuses compared to distressed fetuses. The behavior of H-FHRs reported in this work was in accordance with the results reported in [3,4,9].…”

Section: Discussionsupporting

confidence: 91%

“…Complexity analysis of fetal heart rate (FHR) recordings are constantly developed to discriminate distressed fetuses due to IUGR. Such complexity techniques have been employed to detect and classify signals into healthy and distressed classes [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Advanced Discrimination between Healthy and Intrauterine Growth Restricted Fetuses by Unbiased Recurrence Plots

Zaylaa¹,

Charara²,

Girault³

2015

Adv Tech Biol Med

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“…From the 10 signals which were issued for 3 min, 10 DETs were evaluated using Eq. (2). The average value of DET, expressed as a percentage, was obtained for both the healthy and distressed fetuses.…”

Section: Detection Of Dynamic Transitionsmentioning

confidence: 99%

“…Complexity analysis of biomedical time series by means of various descriptors including, but not limited to, fractal dimension [1], multi-fractal spectra [2], and entropies [3][4][5] is quantitative and currently standard practice.…”

Section: Introductionmentioning

confidence: 99%

Reducing sojourn points from recurrence plots to improve transition detection: Application to fetal heart rate transitions

Zaylaa

Charara

Girault

2015

Computers in Biology and Medicine

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The analysis of biomedical signals demonstrating complexity through recurrence plots is challenging. Quantification of recurrences is often biased by sojourn points that hide dynamic transitions. To overcome this problem, time series have previously been embedded at high dimensions. However, no one has quantified the elimination of sojourn points and rate of detection, nor the enhancement of transition detection has been investigated. This paper reports our on-going efforts to improve the detection of dynamic transitions from logistic maps and fetal hearts by reducing sojourn points. Three signal-based recurrence plots were developed, i.e. embedded with specific settings, derivative-based and m-time pattern. Determinism, cross-determinism and percentage of reduced sojourn points were computed to detect transitions. For logistic maps, an increase of 50% and 34.3% in sensitivity of detection over alternatives was achieved by m-time pattern and embedded recurrence plots with specific settings, respectively, and with a 100% specificity. For fetal heart rates, embedded recurrence plots with specific settings provided the best performance, followed by derivative-based recurrence plot, then unembedded recurrence plot using the determinism parameter. The relative errors between healthy and distressed fetuses were 153%, 95% and 91%. More than 50% of sojourn points were eliminated, allowing better detection of heart transitions triggered by gaseous exchange factors. This could be significant in improving the diagnosis of fetal state.

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“…Among the possible complexity descriptors, fractal [4][5][6][7], multifractal [8][9][10][11][12], recurrent [1,13] and entropy [14][15][16][17][18] indicators are undoubtedly the most effective.…”

Section: Introductionmentioning

confidence: 99%

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals

Zaylaa

Oudjemia

Charara

et al. 2015

Computers in Biology and Medicine

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This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.

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Coarse-Grained Multifractality Analysis Based on Structure Function Measurements to Discriminate Healthy from Distressed Foetuses

Cited by 5 publications

References 27 publications

Advanced Discrimination between Healthy and Intrauterine Growth Restricted Fetuses by Unbiased Recurrence Plots

Advanced Discrimination between Healthy and Intrauterine Growth Restricted Fetuses by Unbiased Recurrence Plots

Reducing sojourn points from recurrence plots to improve transition detection: Application to fetal heart rate transitions

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals

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