Abstract
Preterm delivery increases the risk of infant mortality and morbidity, and therefore developing reliable methods for predicting its likelihood are of great importance. Previous work using uterine electromyography (EMG) recordings has shown that they may provide a promising and objective way for predicting risk of preterm delivery. However, to date attempts at utilizing computational approaches to achieve sufficient predictive confidence, in terms of area under the curve (AUC) values, have not achieved the high discrimination accuracy that a clinical application requires. In our study, we propose a new analytical approach for assessing the risk of preterm delivery using EMG recordings which firstly employs Empirical Mode Decomposition (EMD) to obtain their Intrinsic Mode Functions (IMF). Next, the entropy values of both instantaneous amplitude and instantaneous frequency of the first ten IMF components are computed in order to derive ratios of these two distinct components as features. Discrimination accuracy of this approach compared to those proposed previously was then calculated using six differently representative classifiers. Finally, three different electrode positions were analyzed for their prediction accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in prediction accuracy of preterm delivery risk compared with previous approaches, achieving an impressive maximum AUC value of 0.986 when using signals from an electrode positioned below the navel. In sum, this provides a promising new method for analyzing uterine EMG signals to permit accurate clinical assessment of preterm delivery risk.
Highlights
Preterm delivery, or premature birth, is defined as a baby being born at less than 37 weeks gestation, whereas term delivery implies birth occurring at 37–42 weeks[1]
It should be noted that the p values across the diagonal in Figs 5 and 6 were not considered in the analysis and so in Fig 5 the number of Support Vector Machine (SVM) Random Forests (RF) Multilayer Perception (MLP) AdaBoost (AB) Bayesian Network (BN) Simple Logistic Regression (SLR)
The results show that the area under the curve (AUC) values computed by the Empirical Mode Decomposition (EMD) method were significantly larger than by the non-EMD method (p = 0.0318)
Summary
Premature birth, is defined as a baby being born at less than 37 weeks gestation, whereas term delivery implies birth occurring at 37–42 weeks[1]. Preterm delivery of babies increases their risk of mortality and morbidity and has a comparatively high average incidence of 5–9% of births in developed countries, in the USA even higher figures of PLOS ONE | DOI:10.1371/journal.pone.0132116. The World Health Organization (WHO) has estimated that about one-third of low birth weight deliveries are caused by preterm delivery [3]. Nearly 10% of neonatal mortality worldwide (500,000 deaths per year) are due to preterm delivery [3]. In 2007, Institute of Medicine (IOM) reported that the annual cost associated with 550,000 premature babies born each year in the USA could reach up to $26 billion [7]. Any approach which can effectively predict the likely risk of preterm delivery with sufficient reliability to permit appropriate medical intervention will be of great value
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