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Network Theory Based EHG Signal Analysis and its Application in Preterm Prediction
Insights
This study uses electrohysterogram (EHG) signals to identify preterm birth risk. Network analysis of EHG data improves classification accuracy for term versus preterm pregnancies.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Obstetrics
Background:
- Preterm birth is a major cause of infant mortality.
- Early detection of preterm birth risk is crucial for intervention.
- Uterine electrical activity is linked to contractions.
Purpose of the Study:
- To develop a precise method for classifying term and preterm pregnancies.
- To extract effective features from electrohysterogram (EHG) signals.
- To improve early identification of high-risk pregnancies.
Main Methods:
- Utilized Horizontal Visibility Graph (HVG) algorithm for network representation of EHG signals.
- Applied Short-Time Fourier Transform (STFT) for time-frequency domain analysis.
- Employed feature selection and Partition-Synthesis for imbalanced data to train Support Vector Machine (SVM) classifiers.
Main Results:
- Network-based features identified essential frequency components related to preterm birth.
- Achieved high classification performance with SVM: 0.89 sensitivity, 0.93 specificity, 0.91 accuracy, and 0.97 AUC.
- Demonstrated improved classification of term/preterm pregnancies.
Conclusions:
- Network analysis of EHG signals offers a promising approach for preterm birth prediction.
- The proposed method enhances the accuracy of identifying preterm birth risk.
- This technique can aid in timely medical interventions to prevent preterm birth.
Objective:
Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of high-risk patients followed by medical interventions is essential to the prevention of preterm birth. Based on the relationship between uterine contraction and the fundamental electrical activities of muscles, we extracted effective features from EHG signals recorded from pregnant women, and use them to train classifiers with the purpose of providing high precision in classifying term and preterm pregnancies.
Methods:
To characterize changes from irregularity to coherence of the uterine activity during the whole pregnancy, network representations of the original electrohysterogram (EHG) signals are established by applying the Horizontal Visibility Graph (HVG) algorithm, from which we extract network degree density and distribution, clustering coefficient and assortativity coefficient. Concerns on the interferences of different noise sources embedded in the EHG signal, we apply Short-Time Fourier Transform (STFT) to expand the original signal in the time-frequency domain. This allows a network representation and the extraction of related features on each frequency component. Feature selection algorithms are then used to filter out unrelated frequency components. We further apply the proposed feature extraction method to EHG signals available in the Term-Preterm EHG database (TPEHG), and use them to train classifiers. We adopt the Partition-Synthesis scheme which splits the original imbalanced dataset into two sets, and synthesizes artificial samples separately within each subset to solve the problem of dataset imbalance.
Results:
The optimally selected network-based features, not only contribute to the identification of the essential frequency components of uterine activities related to preterm birth, but also to improved performance in classifying term/preterm pregnancies, i.e., the SVM (Support Vector Machine) classifier trained with the available samples in the TPEHG gives sensitivity, specificity, overall accuracy, and auc values as high as 0.89, 0.93, 0.91, and 0.97, respectively.
