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

  • Image & Signal Processing
  • Published:
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Abstract

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 (< 37W G) 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 A c c u r a c y = 95.70%, S e n s i t i v i t y = 98.40%, S p e c i f i c i t y = 93.00% and A U C = 0.95. Results of the three-channel combination (9 features) were A c c u r a c y = 92.30%, S e n s i t i v i t y = 93.00%, S p e c i f i c i t y = 91.60% and A U C = 0.96. The best single-channel (8 features) gave the mean values: A c c u r a c y = 90.40%, S e n s i t i v i t y = 93.60% and A U C = 0.94. Thus, the advantage of our approach is the high diagnostic performance at low computational cost.

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Authors and Affiliations

  1. Faculty of electronics and informatics, University of Sciences and Technology Houari Boumediene (USTHB), PB 32 El Alia, Bab Ezzouar, Algiers, 16111, Algeria

    Nafissa Sadi-Ahmed, Baya Kacha, Hamza Taleb & Malika Kedir-Talha

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  1. Nafissa Sadi-Ahmed
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Correspondence to Nafissa Sadi-Ahmed.

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Author N. Sadi-Ahmed declares that she has no conflict of interest. Author B. Kacha declares that she has no conflict of interest. Author H. Taleb declares that he has no conflict of interest. Author M. Kedir-talha declares that she has no conflict of interest.

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Sadi-Ahmed, N., Kacha, B., Taleb, H. et al. Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records. J Med Syst 41, 204 (2017). https://doi.org/10.1007/s10916-017-0847-8

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