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Use of Magnetomyographic (MMG) Signals to Calculate the Dependency Properties of the Active Sensors in Myometrial Activity Monitoring

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Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 (ICANN 2003, ICONIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

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Abstract

The uterus is able to accomplish the remarkable task of maintaining an environment which suppresses uterine burst activity during the development of the fetus but keeps tone and initiates and coordinates the individual firing of myometrial cells. These cells produce organized contractions, causing the expulsion of the fetus from the mother’s body. Past studies suggest that the uterus passes through a preparatory process before entering labor. This can happen preterm, leading to increased risk for the fetus. In order to understand the dynamical properties of the uterus, we have developed a preliminary analysis tool [1] that makes it possible to identify where and how the contraction starts and ends, and how it progresses, in terms of intensity, length, and path. Using these properties, we are hoping to differentiate false labor from true labor using the intercellular activities. The myometrial activity modeling (MyAM) will help to determine the regions of localized activation, propagation velocity and direction, and the spread of activity as a function of distance. Through a better understanding of the electrophysiological basis of uterine contractions, better interventions (e.g., drug) can be undertaken which may be effective for suppression of preterm labor or induction of labor at term. Current methodologies lack sensitivity and specificity for prediction of labor, so it is difficult to plan appropriate interventions [2] [3]. The MyAM will provide more detailed and precise information regarding the initiation and propagation of electromyographic activity in the human uterus.

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References

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© 2003 Springer-Verlag Berlin Heidelberg

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Bayrak, C. et al. (2003). Use of Magnetomyographic (MMG) Signals to Calculate the Dependency Properties of the Active Sensors in Myometrial Activity Monitoring. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_124

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  • DOI: https://doi.org/10.1007/3-540-44989-2_124

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

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