A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor

[EN] Although research studies using electrohysterography on women without tocolytic therapy have shown its potential for preterm birth diagnosis, tocolytics are usually administered in emergency rooms at the first sign of threatened preterm labor (TPL). Information on the uterine response during tocolytic treatment could prove useful for the development of tools able to predict true preterm deliveries under normal clinical conditions. The aim of this study was thus to analyze the effects of Atosiban on Electrohysterogram (EHG) parameters and to compare its effects on women who delivered preterm (WDP) and at term (WDT). Electrohysterograms recorded in different Atosiban therapy stages (before, during and after drug administration) on 40 WDT and 27 WDP were analyzed by computing linear, and non-linear EHG parameters. Results reveal that Atosiban does not greatly affect the EHG signal amplitude, but does modify its spectral content and reduces the energy associated with the fast wave high component in both WDP and WDT, with a faster response in the latter. EHG signal complexity remained constant in WDT, while it increased in WDP until it reached similar values to WDT during Atosiban treatment. The spectral and complexity parameters were able to separate (p < 0.05) WDT and WDP prior to and during tocolytic treatment and before and after treatment, respectively. The results pave the way for developing better and more reliable medical decision support systems based on EHG for preterm delivery prediction in TPL women in clinical scenarios.This work received financial support from the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (DPI2015-68397-R), VLC/Campus (UPV-FE-2018-B03) and by Conselleria de Educación, Investigación, Cultura y Deporte, Generalitat Valenciana (GV/2018/104).Mas-Cabo, J.; Prats-Boluda, G.; Ye Lin, Y.; Alberola Rubio, J.; Perales, A.; Garcia-Casado, J. (2019). Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor. Biomedical Signal Processing and Control. 52:198-205. https://doi.org/10.1016/j.bspc.2019.04.001S1982055

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier

Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment

[EN] As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (The ability of EHG recordings to predict imminent labor (<7days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. 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