Journal of
Engineering and Technology Research

  • Abbreviation: J. Eng. Technol. Res.
  • Language: English
  • ISSN: 2006-9790
  • DOI: 10.5897/JETR
  • Start Year: 2009
  • Published Articles: 178

Full Length Research Paper

New technique based on uterine electromyography nonlinearity for preterm delivery detection

Safaa M. Naeem
  • Safaa M. Naeem
  • Department of Biomedical Engineering, Helwan University, Helwan, Egypt.
  • Google Scholar
Ahmed F. Seddik
  • Ahmed F. Seddik
  • Department of Biomedical Engineering, Helwan University, Helwan, Egypt.
  • Google Scholar
Mohamed A. Eldosoky
  • Mohamed A. Eldosoky
  • Department of Biomedical Engineering, Helwan University, Helwan, Egypt.
  • Google Scholar


  •  Received: 27 October 2014
  •  Accepted: 27 October 2014
  •  Published: 03 November 2014

Abstract

Detecting uterine electromyography (EMG) signals can yield a promising approach to determine and take actions to prevent preterm deliveries. This paper objective is to predict this risk using such uterine signals. Previous classification studies have used only linear signal processing which depends on the spectral characteristics of the uterine EMG signals that did not give clinically acceptable results. On the other hand some studies have made linear and non-linear analysis for the signals and have found that the non-linear parameters can distinguish the preterm delivery in better way than the linear parameters. In this research, two methods will be taken; the first method is to take some linear parameters to a suitable neural network and the second one is to take some non-linear parameters to the same network. Then, the two results are compared by calculating parameters False Positive Rate, False Negative Rate, True Positive Rate, True Negative Rate and Accuracy to evaluate the classification performance. Besides, a linear parameter, discrete cosine transform, which depends on the spectral characteristics of the signals, is taken as an additional feature to the same network so the research will have a third method to illustrate the difference between the traditional previous classification method and the proposed ones. Applying the second method gives better results than the first and the third methods. The paper can propose a method depends on the uterine EMG nonlinearity which gives best results to detect preterm delivery compared with those used in previous studies.
           
Key words: Uterine electromyography (EMG) signals, term-preterm deliveries prediction, neural network performance evaluation, discrete cosine transform.