Prediction of Preterm Delivery from Unbalanced EHG Database

Abstract

Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal.

Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager-Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case.

Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%.

Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.

Keywords: electrohysterogram; empirical mode decomposition; prediction; preterm labor; support vector machine.

Figure 1

The block diagram for the discrimination of the deliveries.

Figure 2

The placement of EHG electrodes, adopted from [46].

Figure 3

Examples of the EHG signals from all three channels.

Figure 4

The distribution of the extracted features from IMF1 (first row) and IMF2 (second row) from (a) $C{H}_1$, (b) $C{H}_2$, and (c) $C{H}_3$.

Figure 5

The performance comparison of all three classifiers. * stands for p < 0.05.

Figure 6

ROC of all classifiers with their best performance.