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Dynamic Behavior of Uterine Contractions: An Approach Based on Source Localization and Multiscale Modeling

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Knowledge and Systems Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 326))

Abstract

Among the complex systems of systems of the human body, the uterus is one of the least understood. The sequence of contraction of the myometrium results from numerous interconnected control systems (electric, hormonal, mechanical). In this paper we try to understand what the electrical activity of the uterus can tell us about uterine synchronization. This work is the first attempt to locate uterine EMG sources by means of source localization tools. These tools are tested on signals simulated by a multiscale model of the uterine EMG. As a preliminary result, we present the source localization for one pregnancy and one labor contraction recorded on the same woman. The localized sources do not demonstrate any “propagation like” behavior. This supports the hypothesis that the global organ-level communication is probably not done via action potential propagation, but may be via a coupling between the electrical and the mechanical systems controlling the uterus.

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References

  1. Becker, H., Albera, L., Comon, P., Haardt, M., Birot, G., Wendling, F., Gavaret, M., Bénar, C.G., Merlet, I.: EEG extended source localization: Tensor-based vs. conventional methods. NeuroImage 96, 143–157 (2014)

    Google Scholar 

  2. Dale, A.M., Sereno, M.I.: Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach. Journal of Cognitive Neuroscience 5(2), 162–176 (1993)

    Article  Google Scholar 

  3. Devedeux, D., Marque, C., Mansour, S., Germain, G., Duchene, J.: Uterine electromyography: a critical review. American Journal of Obstetrics and Gynecology 169(6), 1636–1653 (1993), PMID: 8267082

    Google Scholar 

  4. Euliano, T.Y., Marossero, D., Nguyen, M.T., Euliano, N.R., Principe, J., Edwards, R.K.: Spatiotemporal electrohysterography patterns in normal and arrested labor. American Journal of Obstetrics and Gynecology 200(1), 54.e1–54.e7 (2009)

    Google Scholar 

  5. Fuchs, M., Wagner, M., Köhler, T., Wischmann, H.A.: Linear and nonlinear current density reconstructions. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society 16(3), 267–295 (1999), PMID: 10426408

    Google Scholar 

  6. Garfield, R.: Control and assessment of the uterus and cervix during pregnancy and labour. Human Reproduction Update 4(5), 673–695 (1998)

    Article  Google Scholar 

  7. Garfield, R.E., Maul, H., Shi, L., Maner, W., Fittkow, C., Olsen, G., Saade, G.R.: Methods and devices for the management of term and preterm labor. Annals of the New York Academy of Sciences 943(1), 203–224 (2001)

    Article  Google Scholar 

  8. Gramfort, A., Papadopoulo, T., Olivi, E., Clerc, M.: OpenMEEG: opensource software for quasistatic bioelectromagnetics. BioMedical Engineering OnLine 9(1), 45 (2010), PMID: 20819204

    Google Scholar 

  9. Grech, R., Cassar, T., Muscat, J., Camilleri, K.P., Fabri, S.G., Zervakis, M., Xanthopoulos, P., Sakkalis, V., Vanrumste, B.: Review on solving the inverse problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation 5(1), 25 (2008), PMID: 18990257

    Google Scholar 

  10. Hall, W.S.: The Boundary Element Method. Kluwer (1994)

    Google Scholar 

  11. Hassan, M., Boudaoud, S., Terrien, J., Karlsson, B., Marque, C.: Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram. IEEE Transactions on Biomedical Engineering 58(9), 2441–2447 (2011)

    Article  Google Scholar 

  12. Hassan, M., Terrien, J., Karlsson, B., Marque, C.: Interactions between uterine EMG at different sites investigated using wavelet analysis: Comparison of pregnancy and labor contractions. EURASIP J. Adv. Signal Process., 17:1–17:10 (March 2010)

    Google Scholar 

  13. Hassan, M.M., Terrien, J., Muszynski, C.: A Alexandersson, C. Marque, and B. Karlsson. Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography. IEEE Transactions on Biomedical Engineering 60(4), 1160–1166 (2013)

    Article  Google Scholar 

  14. Hauk, O.: Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data. NeuroImage 21(4), 1612–1621 (2004)

    Article  Google Scholar 

  15. Hauk, O., Wakeman, D.G., Henson, R.: Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics. NeuroImage 54(3), 1966–1974 (2011)

    Article  Google Scholar 

  16. Jacob, F.: The logic of life: a history of heredity. Vintage books. Vintage Books (1976)

    Google Scholar 

  17. Koenigsberger, M., Sauser, R., Lamboley, M., Bény, J.-L., Meister, J.-J.: Ca2+ dynamics in a population of smooth muscle cells: modeling the recruitment and synchronization. Biophys. J. 87(1), 92–104 (2004)

    Google Scholar 

  18. Kybic, J., Clerc, M., Abboud, T., Faugeras, O., Keriven, R., Papadopoulo, T.: A common formalism for the integral formulations of the forward EEG problem. IEEE Transactions on Medical Imaging 24(1), 12–28 (2005)

    Article  Google Scholar 

  19. Laforet, J., Marque, C.: uemg: Uterine emg simulator. In: 7th International Workshop on Biosignal Interpretation, BSI 2012, Como, Italy (2012)

    Google Scholar 

  20. Laforet, J., Rabotti, C., Mischi, M., Marque, C.: Improved multi-scale modeling of uterine electrical activity. IRBM 34(1), 38–42 (2013)

    Article  Google Scholar 

  21. Laforet, J., Rabotti, C., Terrien, J., Mischi, M., Marque, C.: Toward a multiscale model of the uterine electrical activity. IEEE Transactions on Biomedical Engineering 58(12), 3487–3490 (2011)

    Article  Google Scholar 

  22. López, J.D., Litvak, V., Espinosa, J.J., Friston, K., Barnes, G.R.: Algorithmic procedures for bayesian MEG/EEG source reconstruction in SPM. NeuroImage 84, 476–487 (2014)

    Article  Google Scholar 

  23. 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.e1–228.10 (2011)

    Google Scholar 

  24. Marque, C., Duchene, J.: Human abdominal EHG processing for uterine contraction monitoring. Biotechnology 11, 187–226 (1989)

    Google Scholar 

  25. Marque, C., Laforet, J., Rabotti, C., Alexandersson, A., Germain, G., Gondry, J., Karlsson, B., Leskosek, B., Mischi, M., Muszinski, C., Oei, G., Peuscher, J., Rudel, D.: A multiscale model of the electrohysterogram the BioModUE_PTL project. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7448–7451 (July 2013)

    Google Scholar 

  26. Mattout, J., Phillips, C., Penny, W.D., Rugg, M.D., Friston, K.J.: MEG source localization under multiple constraints: An extended bayesian framework. NeuroImage 30(3), 753–767 (2006)

    Article  Google Scholar 

  27. Mideksa, K.G., Hellriegel, H., Hoogenboom, N., Krause, H., Schnitzler, A., Deuschl, G., Raethjen, J., Heute, U., Muthuraman, M.: Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals. In: Conference Proceedings:... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 4903–4906 (2012), PMID: 23367027

    Google Scholar 

  28. Miyoshi, H., Boyle, M.B., MacKay, L.B., Garfield, R.E.: Gap junction currents in cultured muscle cells from human myometrium. American Journal of Obstetrics and Gynecology 178(3), 588–593 (1998)

    Article  Google Scholar 

  29. Montes-Restrepo, V., Mierlo, P.V., Strobbe, G., Staelens, S., Vandenberghe, S., Hallez, H.: Influence of skull modeling approaches on EEG source localization. Brain Topography 27(1), 95–111 (2014)

    Article  Google Scholar 

  30. Parthimos, D., Edwards, D.H., Griffith, T.M.: Minimal model of arterial chaos generated by coupled intracellular and membrane ca2+ oscillators. Am. J. Physiol. 277(3 Pt. 2), H1119–H1144 (1999)

    Google Scholar 

  31. Pirondini, E., Babadi, B., Lamus, C., Brown, E.N., Purdon, P.L.: A spatially-regularized dynamic source localization algorithm for EEG. In: Conference Proceedings:... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 6752–6755 (2012), PMID: 23367479

    Google Scholar 

  32. Planes, J.G., Morucci, J.P., Grandjean, H., Favretto, R.: External recording and processing of fast electrical activity of the uterus in human parturition. Medical and Biological Engineering and Computing 22(6), 585–591 (1984)

    Article  Google Scholar 

  33. Rabotti, C., Mischi, M., Beulen, L., Oei, S.G., Bergmans, J.W.M.: Modeling and identification of the electrohysterographic volume conductor by high-density electrodes. IEEE Trans. Biomed. Eng. 57, 519–527 (2010)

    Google Scholar 

  34. Rabotti, C., Mischi, M., Oei, S.G., Bergmans, J.W.M.: Noninvasive estimation of the electrohysterographic Action-Potential conduction velocity. IEEE Transactions on Biomedical Engineering 57(9), 2178–2187 (2010)

    Article  Google Scholar 

  35. Rihana, S., Terrien, J., Germain, G., Marque, C.: Mathematical modeling of electrical activity of uterine muscle cells. Medical & Biological Engineering & Computing 47(6), 665–675 (2009)

    Article  Google Scholar 

  36. Shirvany, Y., Edelvik, F., Persson, M.: Multi-dipole EEG source localization using particle swarm optimization. In: Conference Proceedings:... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 6357–6360 (2013), PMID: 24111195

    Google Scholar 

  37. Young, R.C.: Myocytes, myometrium, and uterine contractions. Ann. N. Y. Acad. Sci. 1101, 72–84 (2007)

    Google Scholar 

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Correspondence to Catherine Marque .

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Marque, C., Diab, A., Laforêt, J., Hassan, M., Karlsson, B. (2015). Dynamic Behavior of Uterine Contractions: An Approach Based on Source Localization and Multiscale Modeling. In: Nguyen, VH., Le, AC., Huynh, VN. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-319-11680-8_42

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  • DOI: https://doi.org/10.1007/978-3-319-11680-8_42

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11679-2

  • Online ISBN: 978-3-319-11680-8

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