Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records

In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37th week of gestation (WG)). It only processes non-invasive electrohysterographic (EHG) signals fully automatically without assistance of an expert or an additional medical system. We used term and preterm EHG signals of 30-minutes duration collected between the 27th and the 32nd WG. Preterm deliveries (< 37WG) had occurred in average 4.00 ± 1.88 weeks since recording dates. Each recording contains three bipolar channels. Using the Huang-Hilbert transform (HHT), we obtained up to twelve intrinsic mode functions (IMFs) for each signal. We found that the most relevant IMFs for both term and preterm records were IMF3 and IMF6. From these two IMFs, we extracted 8 most relevant features targeting EHG signal specificities. We investigated features classifications using support vector machine (SVM) for the 3 single-channels and for all their possible combinations. High discrimination power between term and preterm EHG records was obtained with linear-SVM classifiers. For almost all the cases, mean areas under curves (AUC) exceeded 0.92. A two-channel combination (7 features) achieved the best mean results with Accuracy = 95.70%, Sensitivity = 98.40%, Specificity = 93.00% and AUC = 0.95. Results of the three-channel combination (9 features) were Accuracy = 92.30%, Sensitivity = 93.00%, Specificity = 91.60% and AUC = 0.96. The best single-channel (8 features) gave the mean values: Accuracy = 90.40%, Sensitivity = 93.60% and AUC = 0.94. Thus, the advantage of our approach is the high diagnostic performance at low computational cost.