Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography

Abstract

Objective: Preterm birth is the leading cause of morbidity and mortality involving over 10% of infants. Tools for timely diagnosis of preterm birth are lacking and the underlying physiological mechanisms are unclear. The aim of the present study is to improve early assessment of pregnancy progression by combining and optimizing a large number of electrohysterography (EHG) features with a dedicated machine learning framework.

Methods: A set of reported EHG features are extracted. In addition, novel cross and multichannel entropy and mutual information are employed. The optimal feature set is selected using a wrapper method according to the accuracy of the leave-one-out cross validation. An annotated database of 74 EHG recordings in women with preterm contractions was employed to test the ability of the proposed method to recognize the onset of labor and the risk of preterm birth. Difference between using the contractile segments only and the whole EHG signal was compared.

Results: The proposed method produces an accuracy of 96.4% and 90.5% for labor and preterm prediction, respectively, much higher than that reported in previous studies. The best labor prediction was observed with the contraction segments and the best preterm prediction achieved with the whole EHG signal. Entropy features, particularly the newly-employed cross entropy contribute significantly to the optimal feature set for both labor and preterm prediction.

Significance: Our results suggest that changes in the EHG, particularly the regularity, might manifest early in pregnancy. Single-channel and cross entropy may therefore provide relevant prognostic opportunities for pregnancy monitoring.