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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Obstetrics

    Background:

    • Electrohysterogram (EHG) signals reflect uterine electrical activity during pregnancy.
    • EHG analysis shows potential for predicting and preventing preterm labor.
    • Accurate detection of preterm labor is crucial for neonatal outcomes.

    Purpose of the Study:

    • To develop an efficient, low-computational complexity algorithm for preterm labor detection using EHG signals.
    • To extract relevant features from EHG signals for accurate classification.
    • To evaluate the performance of the proposed algorithm in distinguishing between term and preterm deliveries.

    Main Methods:

    • Empirical Mode Decomposition (EMD) was used for feature extraction from EHG signals.
    • Root Mean Square (RMS) of the first two Intrinsic Mode Functions (IMFs) were utilized as features.
    • Support Vector Machine (SVM) was employed for signal classification.

    Main Results:

    • The algorithm achieved a high accuracy of 99.56% in classifying preterm labor.
    • Sensitivity reached 98.95% and specificity was 99.30% with the optimal configuration.
    • Classification based on the RMS of the second IMF from channel one yielded the best performance.

    Conclusions:

    • The proposed EHG analysis algorithm offers an efficient and accurate method for preterm labor detection.
    • This approach has significant potential for clinical application in monitoring high-risk pregnancies.
    • The low computational complexity makes the algorithm suitable for real-time applications.