title: Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.
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.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Ren
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Shuxia
authors.initials: S
authors.lastname: Yao
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
authors.forename: Jingxuan
authors.initials: J
authors.lastname: Li
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Pedro A
authors.initials: PA
authors.lastname: Valdes-Sosa
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Keith M
authors.initials: KM
authors.lastname: Kendrick
mesh_terms: D019540:Area Under Curve; D004576:Electromyography; D005260:Female; D006801:Humans; D007752:Obstetric Labor, Premature / Q000175:diagnosis* / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D011247:Pregnancy; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D014599:Uterus / Q000503:physiopathology*