Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals

@inproceedings{Ren2015ImprovedPO,
  title={Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals},
  author={Peng Ren and Shuxia Yao and Jingxuan Li and Pedro A. Valdes-Sosa and Keith M. Kendrick and Mikhail A. Lebedev},
  booktitle={PloS one},
  year={2015}
}
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… CONTINUE READING
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