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: 184

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

References

Diab MO, El-Merhie A, El-Halabi N, Khoder L (2010). "Classification of uterine EMG signals using supervised classification method." J. Biomed. Sci. Eng. 3:837-842.
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Diab MO, Marque C, Khalil MA (2007). "Classification for Uterine EMG Signals:
Comparison between AR Model and Statistical Classification Method." IJCC.
 
 
Moslem B, Diab MO, Khalil MA, Marque C (2012). "Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals,"  EURASIP J. Adv. Signal Process. 2012.1 (2012): 1-9.
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Garfield RE, Maner WL, MacKay LB, Schlembach D, Saade GR (2005). "Comparing uterine electromyography activity of antepartum patients versus term labor patients," Am. J. ObstetGynecol. 193:23-29.
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Maul H, Maner WL, Olson G, Saade GR, Garfield RE (2004). "Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery," J Matern. Fetal Neonatal Med. 15:297-301.
Crossref
 
 
Hassan M, Terrien J, Alexandersson A, Marque C, Karlsson B (2010). "Nonlinearity of EHG signals used to distinguish active labor from normal pregnancy contractions," In Proceedings of the 32nd Annual International Conference of the IEEE EMBS: 31 August- 4 September 2010; Buenos Aires, Argentina.
 
 
Maner WL, Garfield RE (2007). "Identification of human term and preterm labor using artificial neural networks on uterine electromyography data," Ann. Biomed. Eng. 35:465-473.
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Fele-Žorž G, Kavšek G, Novak-Antolič Z, Jager F (2008). "A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups," Med. Biol. Eng. Comput. 46:911-922.
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Akay M (2001). Nonlinear biomedical signal processing. In dynamic analysis and modeling, IEEE Inc. New York. 2.
 
 
Naeem SM, Ali AF, Eldosoky MA (2013). "Comparison between Using Linear and Non-linear Features to Classify Uterine Electromyography Signals of Term and Preterm Deliveries," In Proceedings of the 30th National Radio
Science Conference NRSC: 16-18 April 2013; Cairo, Egypt. pp. 488-498.
 
 
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Jovic A, Bogunovic N (2007). "Feature extraction for ECG time-series mining based on chaos theory," In Proceedings of the ITI 29th Int. Conf. on Information Technology Interfaces: 25-28June 2007; Cavtat, Croatia.
 
 
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Yung NK (2009). Singular Spectrum Analysis, Master's Thesis. University of California, Los Angeles.
 
 
Nielsen F (2001). Neural Networks – algorithms and applications, Niels Brock Business College
 
Goyal S, Goyal GK (2011). Cascade and Feed-forward Backpropagation rtificial Neural Network Models for Prediction of Sensory Quality of Instant Coffee Flavoured Sterilized Drink," Can. J. Artificial Intelligence, Mach. Learn. Pattern recognition.

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