Delta-fuzzy similarity entropy to discriminate healthy from sick fetus

Oudjemia, Souad; Zaylaa, Amira; Charara, Jamal; Girault, Jean‐Marc

doi:10.1109/icabme.2013.6648832

Cited by 5 publications

(9 citation statements)

References 11 publications

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“…The straightforward comparison of the cross-diagonal values with the detected points forming the cross-diagonal lines obtained through the reference method, RP 2 , showed that (i) 27% out of 22% cross-diagonal points were detected in RP 3 , (ii) 13.3% out of 22% cross-diagonal points were detected in RP 4 , and (iii) 13% out of 22% cross-diagonal points were detected in RP 5 .…”

Section: Quantitative Evaluation Of Sojourn Point Reductionmentioning

confidence: 96%

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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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Section: Quantitative Evaluation Of Sojourn Point Reductionmentioning

confidence: 96%

“…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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“…SD that measures the amount of RQA i dispersion from its average value is given as follows: (8) where N is the total number of FHRs used.…”

Section: Statistical Tools For Evaluating the Discriminationmentioning

confidence: 99%

“…Some of these tools were quantitative, such as the entropy (En) analysis and types including, but not limited to, the approximated entropy, sample entropy, fuzzy entropy and recently n-order entropy [4,5,7,8,11]. Other methods were qualitative such as the Recurrence Plots (RPs) and their corresponding Recurrence Quantification Analysis (RQA) [2,3,10].…”

Section: Introductionmentioning

confidence: 99%

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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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Cascade of nonlinear entropy and statistics to discriminate fetal heart rates

Zaylaa

Saleh

Karameh

et al. 2016

2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)

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Fetal heart rate discrimination is an evolving field in biomedical engineering with many efforts dedicated to avoid preterm deliveries by way of improving fetus monitoring methods and devices. Entropy analysis is a nonlinear signal analysis technique that has been progressively developed to improve the discriminability of a several physiological signals, with Kernel based entropy parameters (KBEPs) found advantageous over standard techniques. This study is the first to apply KBEPs to analyze fetal heart rates. Specifically, it explores the usability of the cutting-edge nonlinear KBEPs in discriminating between healthy fetuses and fetuses under distress. The database used in this study comprises 50 healthy and 50 distressed fetal heart rate signals with severe intrauterine growth restriction. The Cascade analysis investigates six kernel based entropy measures on fetal heart rates discrimination, and compares them to four standard entropies. The study presents a statistical evaluation of the discrimination power of each parameter (paired t-test statistics and distribution spread). Simulation results showed that the distribution ranges in 80% of the entropy parameters in the distressed heart group are higher than those in the healthy control group. Moreover, the results show that it is advantageous to choose Circular entropy then Cauchy entropy (p < 0.001) over the standard techniques, in order to discriminate fetal heart rates.

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Delta-fuzzy similarity entropy to discriminate healthy from sick fetus

Cited by 5 publications

References 11 publications

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

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

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

Cascade of nonlinear entropy and statistics to discriminate fetal heart rates

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