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

Abstract : 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 kurtosis-based 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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Submitted on : Saturday, October 19, 2019 - 10:18:40 PM
Last modification on : Tuesday, April 5, 2022 - 2:54:01 PM
Long-term archiving on: : Monday, January 20, 2020 - 1:40:23 PM


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  • HAL Id : inserm-02320950, version 1



Rayan Chaaban, Walaa Issa, Ayache Bouakaz, Amira Zaylaa. Hypertensive Disorders of Pregnancy: Kurtosis-Based Classification of Fetal Doppler Ultrasound Signals. The 5th International Conference on Advances in Biomedical Engineering, IEEE-EMBS, Oct 2019, Tripoli, Lebanon. ⟨inserm-02320950⟩



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