A Bi-spectrum Analysis of Uterine Electromyogram Signal Towards the Prediction of Preterm Birth

  • Kamalraj SubramaniamEmail author
  • P. Shaniba Asmi
  • Nisheena V. Iqbal
Part of the Intelligent Systems Reference Library book series (ISRL, volume 172)


Prediction of preterm birth is one of the significant perinatal hurdles for the prevention of preterm birth. The uterine Electromyogram (Uterine EMG), obtained from the abdominal surface is analyzed for the prediction or preterm labor. Many linear and non-linear features and classifiers have been analyzed in different researches. In this paper two neural network classifiers were applied to the Bi-spectrum feature obtained from the Uterine EMG signal. The Bi-spectrum analysis was done after preprocessing the signal. Three pre-processing methods were tried to improve the performance. The best classification accuracy of 99.89% was obtained with Elman neural network classifier when pre-processed with three level wavelet (db4) decomposition. The sensitivity and specificity were found to be 100% and 99.77% respectively.


Electromyogram Bi-spectrum analysis Elman neural network classifier 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kamalraj Subramaniam
    • 1
    Email author
  • P. Shaniba Asmi
    • 2
  • Nisheena V. Iqbal
    • 2
  1. 1.Karpagam Academy of Higher EducationCoimbatoreIndia
  2. 2.MES College of EngineeringKuttippuramIndia

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