Signal, Image and Video Processing

, Volume 8, Issue 6, pp 1169–1178

Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor

  • D. Alamedine
  • A. Diab
  • C. Muszynski
  • B. Karlsson
  • M. Khalil
  • C. Marque
Original Paper


This article proposes a selection method that can be applied to choose the best parameters to classify contractions in the uterine electrohysterography (EHG) signal for the detection of preterm labor. Several types of parameters have historically been extracted from the electrohysterogram. These can be divided into three classes: linear parameters, nonlinear parameters and parameters related to the electrohyterogram propagation. Frequency band enhancement EHG characterization has also been extensively studied. Our work is divided in two parts. The first part is to implement and compute all the parameters already extracted from the EHG that have been published in the literature. These parameters were computed both on the original EHG and on different frequency bands obtained using wavelet packet decomposition. In the second part, we will use a new parameters selection method to eliminate all parameters that are not efficient and pertinent for classification. Our results indicate a set of 13 linear parameters, 3 nonlinear parameters and 2 propagation parameters that are potentially most useful to discriminate between pregnancy and labor contractions, either on different frequency bands or directly on original EHG.


Electrohysterogram Jeffrey divergence methods Preterm labor Parameters selection 

List of symbols


Degree of polynomial function

\(\alpha \)

scaling exponent alpha


Number of bins of histogram




number of pattern matches

D1, D2, D3, D4, D5, D6, D7, D8 and D9



Median frequency


Detrended fluctuation analysis

\(D_{Je} \left( {H,G} \right) \)

Jeffrey divergence

\(\Vert \Delta _{{d}_0 } \Vert \)

Euclidean distance between two states of the system to an arbitrary time \(t_0 \)

\(\Vert \Delta _{{d}_{t}} \Vert \)

Euclidean distance between the two states of the system at a time later \(t\)


Uterine electrohysterographic signal, electrohysterogram


Fluctuation function


Linear piecewise approximation of the nonlinear regression curve


False nearest neighbors

\(\varPhi _{x}(t)\)

Unwrapped phases of the signals x

\(\varPhi _{y}(t)\)

Unwrapped phases of the signals y

\(\varphi _{e,f} \)

Phase synchronization principle

\(\gamma _{e,f}\)

Phase synchronization called “mean phase coherence”

\(H=\{h_{z}\}\) and \(G=\{g_{z}\}\)

\(H\) and \(G\) are the two histograms


Nonlinear correlation coefficient


Instantaneous frequency


Lyapunov exponent


Length of sequence


Mean frequency


Length of box


Length of the signal


Number of sliding windows


Peak frequency


Preterm labor

\(\hbox {PSD}, S_{x}(f)\)

Power spectral density


Tolerance for accepting

\(R^{2 }\)

Linear correlation coefficient


Sample entropy


Mean+1*standard deviation


Mean+2*standard deviation


Time reversibility

\(\tau \)

Time delay


Variance entropy


Bipolar channel 7


Bipolar channel 8


Vertical bipolar signals

W1, W2, W3, W4 and W5

Variances on the five selected detail levels

\(X\left( k \right) \)

New integrated series


EHG bipolar channel Vb7


l-th segment of \(x\)


Mean of x


EHG bipolar channel Vb8


  1. 1.
    Neonatal and perinatal mortality: country, regional and global estimates; Report WHO 2006.
  2. 2.
    Maner, W.L., Garfield, R.E., Maul, H., Olson, G., Saade, G.: Predicting term and preterm delivery with transabdominal uterine electromyography. Obstet. Gynecol. 101(6), 1254–1260 (2003)CrossRefGoogle Scholar
  3. 3.
    Devedeux, D., Marque, C., Mansour, S., Germain, G., Duchêne, J.: Uterine electromyography: a critical review. Am. J. Obstet. Gynecol. 169(6), 1636 (1993)CrossRefGoogle Scholar
  4. 4.
    Maner, W.L., Garfield, R.E.: Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann. Biomed. Eng. 35(3), 465–473 (2007)CrossRefGoogle Scholar
  5. 5.
    Marque, C.K., Terrien, J., Rihana, S., Germain, G.: Preterm labour detection by use of a biophysical marker: the uterine electrical activity. BMC Pregnancy Childbirth 7(Suppl 1), S5 (2007)CrossRefGoogle Scholar
  6. 6.
    Marque, C., Duchene, J.M.G., Leclercq, S., Panczer, G.S., Chaumont, J.: Uterine EHG processing for obstetrical monitorng. IEEE Trans. Biomed. Eng. 12, 1182–1187 (1986)CrossRefGoogle Scholar
  7. 7.
    Leman, H., Marque, C., Gondry, J.: Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans. Biomed. Eng. 46(10), 1222–1229 (1999)CrossRefGoogle Scholar
  8. 8.
    Marque, C., Leman, H., Voisine, M.L., Gondry, J., Naepels, P.: Traitement de l’électromyogramme utérin pour la caractérisation des contractions pendant la grossesse. RBM-News 21(9), 200–211 (1999)CrossRefGoogle Scholar
  9. 9.
    Sikora, J., Matonia, A., Czabanski, R., Horoba, K., Jezewski, J., Kupka, T.: Recognition of premature threatening labour symptoms from bioelectrical uterine activity signals. Arch. Perinat. Med. 17(2), 97–103 (2011)Google Scholar
  10. 10.
    Arora, S., Garg, G.: A novel scheme to classify EHG signal for term and pre-term pregnancy analysis. Int. J. Comput. Appl. 51(18), 37–41 (2012)Google Scholar
  11. 11.
    Diab, M.O., Marque, C., Khalil, M.A.: Classification for uterine EMG signals: comparison between AR model and statistical classification method. Int. J. Comput. Cognit. 5(1), 8–14 (2007)Google Scholar
  12. 12.
    Moslem, B., Khalil, M., Marque, C., Diab, M.O.: Energy distribution analysis of uterine electromyography signals. J. Med. Biol. Eng. 30(6), 361–365 (2010)CrossRefGoogle Scholar
  13. 13.
    Radomski, D., Grzanka, A., Graczyk, S., Przelaskowski, A.: Assessment of uterine contractile activity during a pregnancy based on a nonlinear analysis of the uterine electromyographic signal. In: Information Technologies in Biomedicine. Springer, Berlin, 47(3), 325–331 (2008)Google Scholar
  14. 14.
    Ivancevic, T., Jain, L., Pattison, J., Hariz, A., et al.: Preterm birth analysis using nonlinear methods. Recent Patents Biomed. Eng. 1(3), 160–170 (2008)CrossRefGoogle Scholar
  15. 15.
    Moslem, B., Khalil, M., Diab, M.O., Marque, C.: Detrended fluctuation analysis of uterine electromyography. Presented at the First Middle East Conference on Biomedical Engineering, MECBME11, Sharjah, UAE (2011)Google Scholar
  16. 16.
    Diab, A., Hassan, M., Marque, C., Karlsson, B.: Quantitative performance analysis of four methods of evaluating signal nonlinearity: application to uterine EMG signals. Presented at the 34th Annual International IEEE EMBS Conference. San Diego, USA (2012)Google Scholar
  17. 17.
    Hassan, M., Terrien, J., Alexandersson, A., Marque, C., Karlsson, B.: Improving the classification rate of labor vs. normal pregnancy contractions by using EHG multichannel recordings. Presented at the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Espagnol (2010)Google Scholar
  18. 18.
    Hassan, M., Alexandersson, A., Terrien, J., Muszynski, C., Marque, C., Karlsson, B.: Better pregnancy monitoring using nonlinear propagation analysis of external uterine electromyography. IEEE Trans. Biomed. Eng. 60(4), 1160–1166 (2013)CrossRefGoogle Scholar
  19. 19.
    Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž., Jager, F.: 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(9), 911–922 (2008)CrossRefGoogle Scholar
  20. 20.
    Terrien, J., Steingrimsdottir, T., Marque, C., Karlsson, B.: Synchronization between EMG at different uterine locations investigated using time–frequency ridge reconstruction: comparison of pregnancy and labor contractions. EURASIP J. Adv. Signal Process. 2010, 1–10 (2010)Google Scholar
  21. 21.
    Lucovnik, M., Maner, W.L., Chambliss, L.R., Blumrick, R., Balducci, J., Novak-Antolic, Z., Garfield, R.E.: Noninvasive uterine electromyography for prediction of preterm delivery. Am. J. Obstet. Gynecol. 204(3), 228.e1–228.e10 (2011)CrossRefGoogle Scholar
  22. 22.
    Coifman, R.R., Wickerhauser, M.V.: Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory 38(2), 713–718 (1992)CrossRefMATHGoogle Scholar
  23. 23.
    Khalil, M.: Une approche de la détection et de la classification dans les signaux non stationnaires. Application a l’EMG utérin. Ph.D. dissertation, Thèse de l’université de Technologie de Troyes, 1999. [Bin French]Google Scholar
  24. 24.
    Alamedine, D., Khalil, M., Marque, C.: Comparison of different EHG feature selection methods for the detection of preterm labor. Comput. Math. Methods Med. (2013, in press)Google Scholar
  25. 25.
    Hu, M., Liang, H.: Variance entropy: a method for characterizing perceptual awareness of visual stimulus. Appl. Comput. Intell. Soft Comput. 2012, 1–6 (2012)Google Scholar
  26. 26.
    Bhattacharya, J.: Reduced degree of long-range phase synchrony in pathological human brain. Acta Neurobiol. Exp. (Warsz.) 61(4), 309–318 (2001)Google Scholar
  27. 27.
    Rajaei, A., Dallalzadeh, E., Rangarajan, L.: Symbolic representation and classification of medical X-ray images. Signal Image Video Process. 1–11. doi:10.1007/s11760-013-0486-6
  28. 28.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)CrossRefMATHGoogle Scholar
  29. 29.
    Ma, Y., Gu, X., Wang, Y.: Histogram similarity measure using variable bin size distance. Comput. Vis. Image Underst. 114(8), 981–989 (2010)CrossRefGoogle Scholar
  30. 30.
    Terrien, J., Hassan, M., Germain, G., Marque, C., Karlsson, B.: Nonlinearity testing in the case of non Gaussian surrogates, applied to improving analysis of synchronicity in uterine contraction. Presented at the Engineering in Medicine and Biology Society: EMBC 2009. Annual International Conference of the IEEE, Minneapolis, USA, pp. 3477–3480 (2009)Google Scholar
  31. 31.
    Terrien, J., Marque, C., Germain, G., Karlsson, B.: Sources of bias in synchronization measures and how to minimize their effects on the estimation of synchronicity: Application to the uterine electromyogram. In: Naik G.R. (ed.) Recent Advances in Biomedical Engineering, Chap. 5, pp. 73–99. I-Tech Education and Publishing, Vienne, Autriche (2009)Google Scholar
  32. 32.
    Diab, A., Hassan, M., Karlsson, B., Marque, C.: Effect of decimation on the classification rate of non-linear analysis methods applied to uterine EMG signals. IRBM 34(4–5), 326–329 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • D. Alamedine
    • 1
    • 2
  • A. Diab
    • 1
    • 3
  • C. Muszynski
    • 4
  • B. Karlsson
    • 3
  • M. Khalil
    • 2
  • C. Marque
    • 1
  1. 1.CNRS URM 7338 Biomécanique et Bio-ingénierieUniversité de Technologie de CompiègneCompiègneFrance
  2. 2.Laboratoire LASTRE, EDST-Centre Azm pour la Recherche en Biotechnologie et ses ApplicationsUniversité libanaiseTripoliLiban
  3. 3.School of Science and EngineeringReykjavik UniversityReykjavik Iceland
  4. 4.CGOAmiensFrance

Personalised recommendations