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

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

Section: Discussionmentioning

confidence: 99%

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

Nieto-del-Amor

Beskhani

Ye-Lin

et al. 2021

“…It is well known that as labor approaches, EHG signal amplitude increases due to a major recruitment of cell numbers involved in uterine contractions, and that signal spectral content shifts to a higher frequency because of increased cell excitability [31]. Most et al found significant differences in RMS values between deliveries in more or less than 14 days in healthy women in drug-free condition [54]. It has been reported that dominant frequency computed in the 0.24-4 Hz frequency range in threatened preterm labor women who delivered in less than 7 days and did not receive any tocolytic therapy was significantly higher than those who delivered after 7 days [17].…”

Section: Discussionmentioning

confidence: 99%

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

Mas-Cabo

Prats-Boluda

Garcia-Casado

et al. 2020

Self Cite

Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 ± 4.3% and 76.2 ± 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.

“…Although several studies deal with the use of EHG for preterm labor prediction in women recorded during regular checkups in a drug-free physiological state [ 21 , 22 , 31 , 32 , 34 , 37 , 52 ], the literature is scarce on preterm labor predictive systems in women with TPL under the effect of tocolytic drugs [ 20 ], even though tocolytic drugs are usually clinically administered at the first signs of TPL. These drugs were found to modify the EHG characteristics and these changes are dependent on the phase of the drug administration in which the recordings were made [ 25 , 26 ]. Despite this, the usefulness of EHG for the prediction of imminent delivery in women with TPL under tocolytic treatment has already been checked in a previous study using ANN [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…Labor or preterm labor prediction algorithms have been developed with over 90% accurate metrics [ 21 , 22 , 23 , 24 ] when using EHG recordings from women during clinical checkups under physiological conditions. However tocolytic drugs are usually administered to inhibit uterine contractions at the first sign of threatened preterm labor and modify the EHG features [ 25 , 26 ]. The feasibility of imminent preterm labor prediction in women with TPL undergoing tocolytic treatment by means of an artificial neural network (ANN) algorithm has been proved using median values of EHG parameters in 120 s analysis windows and obstetric data inputs [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

Prats-Boluda

Pastor-Tronch

Garcia-Casado

et al. 2021

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.