Transactions on Machine Intelligence

Transactions on Machine Intelligence

Classification of Abdominal Electromyogram Signals for Detecting Pregnancy Contractions Using Support Vector Machine in the Wavelet Packet Domain

Document Type : Original Article

Authors
S. S. Hasani 1
H. Montazery Kordy 2
1 Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
2 Assistant Professor, Department of Biomedical Engineering, Babol Noshirvani University of Technology, Babol, Iran
10.47176/TMI.2019.246
Abstract
One of the early signs of natural labor is the occurrence of contractions in the abdominal region of a pregnant woman. However, the presence of contractions in the uterus alone is not a definitive indicator of the onset of natural labor. One of the recent research topics has been the processing of abdominal electromyogram signals from pregnant women to detect preterm labor. The objective of this paper is to classify abdominal electromyogram signals into two classes: labor contractions and pregnancy contractions, in order to detect preterm labor. Due to the differences in the energy distribution of abdominal electromyogram signals throughout pregnancy, the signals are decomposed by a three-level wavelet packet transform. The energy of the wavelet packets at the final decomposition level is then calculated and used for signal classification. The results show that the support vector machine is capable of distinguishing pregnancy contractions from labor-induced pain with a classification accuracy of 86%, sensitivity of 88%, and specificity of 83%, based on the energy features of the wavelet packet.
Keywords
Abdominal Electromyogram Signals
Wavelet Packet Energy
Uterine Contractions
Support Vector Machine
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Transactions on Machine Intelligence
Volume 2, Issue 4
Autumn 2019
Pages 246-252

  • Receive Date 02 May 2019
  • Revise Date 20 June 2019
  • Accept Date 22 December 2019