ArticlePloS one2015
Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 77 citations in OpenAlex.
- A deep learning method for preterm birth prediction using wavelet representations and auxiliary features from electrohysterogram.Medical & biological engineering & computing · 2026Article
- Electrohysterography for Uterine Contractility Monitoring: Measurement Principles, Clinical Evidence, and Reporting Recommendations.Sensors (Basel, Switzerland) · 2026Review
- Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE.Scientific reports · 2025Article
- Preterm birth prediction from electrohysterogram using multivariate empirical mode decomposition.Medical & biological engineering & computing · 2025Article
- Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction.Heliyon · 2024Article
- Article
- Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks.Sensors (Basel, Switzerland) · 2023Article
- Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data.Sensors (Basel, Switzerland) · 2022Article
- Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors.Sensors (Basel, Switzerland) · 2022Article
- Prediction of Preterm Delivery from Unbalanced EHG Database.Sensors (Basel, Switzerland) · 2022Article
- Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals.Sensors (Basel, Switzerland) · 2021Review
- Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.Sensors (Basel, Switzerland) · 2021Article
- Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography.Sensors (Basel, Switzerland) · 2021Article
- Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records.Sensors (Basel, Switzerland) · 2020Article
- Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios.Entropy (Basel, Switzerland) · 2020Article
- A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity.Sensors (Basel, Switzerland) · 2020Article
- Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy.Sensors (Basel, Switzerland) · 2020Article
- Using an innovative stacked ensemble algorithm for the accurate prediction of preterm birthJournal of the Turkish German Gynecological Association · 2019Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.