Abstract
Evidence of the relationships between physiological data has been found in previous studies. However there is still limited knowledge about underlying mechanisms and patterns of the preterm birth and direction of propagating uterine contraction. In this paper we study transfer entropy (TE) that is widely used to quantify interactions between biomedical time series. We are searching for indices that could detect preterm labor using 112 contractions extracted from differentiated electrohysterographical (EHG) signals. Transfer entropy was considered as a bivariate approach to quantify the bidirectional information flow from channel 1 to channel 4. TE values for women delivering after 7 days from presenting of threatened preterm labor symptoms were significantly higher than those delivering within 7 days (p < 0.01). The parameters used in this study help to estimate the potential of premature labor as it progresses. Therefore, they may be useful as early risk markers of preterm birth.
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Acknowledgement
Research for female patients was supported by Grant no. N N407 598338 from the National Science Center. The research was performed as a part of the projects S/WM/1/2017 and was financed with the founds for science from the Polish Ministry of Science and Higher Education.
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Borowska, M., Kuć, P. (2019). Transfer Entropy in Quantifying the Interactions in Preterm Labor. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_31
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