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

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

doi:10.4172/2379-1764.1000177

Cited by 4 publications

(4 citation statements)

References 39 publications

(88 reference statements)

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“…According to the features extracted, there are differences in the outcomes of the classification and clustering results of normal and the IUGR signals. The sensitivity, specificity and precision percentages were calculated as in [9], and are shown in Table II. The highest statistical values were highlighted in bold.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Many studies have been carried out on feature extraction from Fetal Heart Rates (FHRs), specifically the use of non-linear features, such as entropies to discriminate FHRs [5], [6], [7], [8], [9]. A study published by Zaylaa et al, focused on the employement of Recurrence Plots to promot the discrimination between normal and IUGR fetuses.…”

Section: Introductionmentioning

confidence: 99%

“…A study published by Zaylaa et al, focused on the employement of Recurrence Plots to promot the discrimination between normal and IUGR fetuses. Entropy and recurrence rates were computed, and the sensitivity, specificity, and accuracy of the recurrence quantification parameters were measured relative to the discrimination of IUGR fetuses [9]. Another study published by Zaylaa et al provided N-order and maximum fuzzy similarity entropy features for improving the discrimination of FHR signals [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hypertensive Disorders of Pregnancy: Kurtosis-Based Classification of Fetal Doppler Ultrasound Signals

Chaaban

Issa

Bouakaz

et al. 2019

2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME)

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Hypertensive Disorders of Pregnancy (HDP), a group of medical conditions occurring during pregnancy, have wide-reaching implications on the normal progress of 17% of pregnancies, leading to maternal and perinatal morbidity and mortality. One of the HDP complications is the Intrauterine Growth Restriction (IUGR). IUGR changes the behavior of any feature extracted from Fetal Heart Rates (FHRs). These features if well-selected improve the classification of IUGR. However, the choice of features was reliant on whether it's linear or nonlinear. Also, the classification algorithms such as, K-Means and Support Vector Machine (SVM) used to predict and classify biomedical signals were not optimal, and the best classification algorithm was not yet set. Our aim is to propose a new kurtosisbased combinations of features and explore their effect on HDP and IUGR classification from Doppler Ultrasound FHRs. Features extracted from FHRs were fed into K-means and SVM classification algorithms. The database comprised 50 normal and 50 IUGR FHRs. Results showed that the best extracted features were those based on kurtosis, and the best classification method was the SVM. The best combination result was 67% sensitive, 100% specific and 100% precise to the classification and detection of IUGR and thus HDP. A further future study could test additional combination of features and other classification-based methods to predict IUGR and thus HDP.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hypertensive Disorders of Pregnancy: Kurtosis-Based Classification of Fetal Doppler Ultrasound Signals

Chaaban

Issa

Bouakaz

et al. 2019

2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME)

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“…Entropy quantification was used for such discrimination and for prediction purposes. Entropy analysis measures the correction and persistence of a signal in a nonlinear mathematical approach to quantify the irregularity or disorder and complexity of a system [7], [8], [9], [10], [11], [12], [13]. Ferrario et al have used both approximate entropy and sample entropy, Ferrario et al have also used ANOVA test [6].…”

Section: Introductionmentioning

confidence: 99%

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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