Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest

Jin Peng, Dongmei Hao, Lin Yang, Mengqing Du, Xiaoxiao Song, Hongqing Jiang, Yunhan Zhang, Dingchang Zheng

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Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26th week of gestation.

Original languageEnglish
Pages (from-to)352-362
Number of pages11
JournalBiocybernetics and Biomedical Engineering
Issue number1
Early online date25 Dec 2019
Publication statusPublished - 2020


Bibliographical note

© 2019 The Author(s). Published by Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Biomedical Engineering ofthe Polish Academy of Sciences. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).


  • Electrohysterogram (EHG)
  • Feature extraction
  • Gestational week
  • Preterm delivery
  • Random forest (RF).

ASJC Scopus subject areas

  • Biomedical Engineering

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