EHG Signal Analysis for Prediction of Term and Preterm using Variational Mode Decomposition and Artificial Neural Networks

Muhammad Umar Khan, Sumair Aziz, Khushbakht Iqtidar, Raul Fernandez Rojas

    Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

    5 Citations (Scopus)

    Abstract

    Preterm deliveries are an important cause of mortality and morbidity in newborns. Accurate and early prediction of a premature delivery can prove helpful in providing proper medication and treatment. Recording of electrical activity known as Electrohysterogram (EHG) from the abdominal surface of pregnant women corresponds to the uterus contractions. A new direction is open using EHG signals for the diagnosis of preterm births. In this research, we present a new method for the accurate classification of preterm and term EHG signals. The proposed method first filters a three-channel EHG signal using bandpass filters. Next, we combined the filtered three-channel EHG into one signal using an accumulation operation. The accumulated EHG signal was post-processed through variational mode decomposition (VMD). VMD algorithm splits the input signal into finite modes using center frequencies known as intrinsic mode functions (IMFs). An energy-based intelligent signal reconstruction approach is designed to combine IMFs having an energy level above the computed threshold. Next, the reconstructed EHG signals were split into continuous windows, and time, frequency, and Hjorth features were extracted. These features were fused to construct a distinct feature representation and were reduced using the ReliefF algorithm. We trained an artificial neural network (ANN) to obtain 98.8 % average accuracy using 10-fold cross-validation.

    Original languageEnglish
    Title of host publicationProceedings - 2022 International Conference on Frontiers of Information Technology, FIT 2022
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages267-272
    Number of pages6
    ISBN (Electronic)9798350345933
    DOIs
    Publication statusPublished - 2022
    Event2022 International Conference on Frontiers of Information Technology, FIT 2022 - Islamabad, Pakistan
    Duration: 12 Dec 202213 Dec 2022

    Publication series

    NameProceedings - 2022 International Conference on Frontiers of Information Technology, FIT 2022

    Conference

    Conference2022 International Conference on Frontiers of Information Technology, FIT 2022
    Country/TerritoryPakistan
    CityIslamabad
    Period12/12/2213/12/22

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