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Updated: Oct 2, 2025

Preterm EEG: A Multimodal Neurophysiological Protocol
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Prediction of Preterm Delivery from Unbalanced EHG Database.

Somayeh Mohammadi Far1, Matin Beiramvand2, Mohammad Shahbakhti3

  • 1AGH University of Science and Technology, 30059 Krakow, Poland.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
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Combination of Empirical Mode Decomposition and Hjorth Parameters for Prediction of Preterm Labor using Electrohysterogram Signals.

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This study introduces an automated algorithm for predicting preterm labor using a single electrohysterogram (EHG) signal, achieving high accuracy. The method simplifies prediction, potentially reducing complications for mothers and infants.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Maternal-Fetal Medicine

Background:

  • Early prediction of preterm labor is crucial for minimizing complications.
  • Electrohysterogram (EHG) signals offer a non-invasive method for monitoring uterine activity.
  • Existing methods may require complex signal processing or multiple channels.

Purpose of the Study:

  • To develop an automated algorithm for preterm labor prediction using a single EHG signal.
  • To evaluate the performance of machine learning classifiers for EHG-based prediction.
  • To establish a computationally efficient and accurate method for early preterm labor detection.

Main Methods:

  • Empirical Mode Decomposition (EMD) was used to decompose EHG signals into intrinsic mode functions (IMFs).
Keywords:
electrohysterogramempirical mode decompositionpredictionpreterm laborsupport vector machine

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  • Feature extraction included sample entropy (SampEn), root mean square (RMS), and mean Teager-Kaiser energy (MTKE) from each IMF.
  • K-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers were employed for prediction.

    • Main Results:

    • The database comprised 262 term and 38 preterm delivery EHG recordings.
    • The SVM classifier with a polynomial kernel achieved the highest performance: 99.5% sensitivity, 99.7% specificity, and 99.7% accuracy.
    • The Decision Tree (DT) classifier demonstrated strong results with 100% sensitivity, 98.4% specificity, and 98.7% accuracy.

    Conclusions:

    • The proposed algorithm effectively predicts preterm labor using a single EHG channel.
    • This approach surpasses state-of-the-art methods by avoiding synthetic data and feature ranking.
    • The single-channel EHG analysis offers a simplified and highly accurate tool for preterm labor prediction.