Summary
Cardiotocographic monitoring (CTG) is the primary biophysical method for assessment of the fetal state. It consists in analysis of fetal heart rate variability, uterine contraction activity and fetal movements signal. Visual analysis of printed cardiotocographic traces is difficult so the computerized fetal monitoring systems are a standard in clinical centres. In the proposed work we investigated the ability of the application of artificial neural networks for the prediction of newborn sex using parameters of quantitative description of CTG traces. We examined the influence of input data representation (numerical or categorical) and the influence of the gestational age on the classification quality. We obtained the classification quality at a level of 80% and therefore we may state, that there is rather a strong relation between the fetal gender and the fetal heart rate variability.
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References
Czogala, E., Leski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Physica-Verlag, Berlin (2000)
Dawes, N.W., Dawes, G.S., Moulden, M., Redman, C.W.G.: Fetal heart rate patterns in term labour vary with sex, gestational age, epidural analgesia, and fetal weight. American Journal of Obstetrics and Gynaecology 180, 181–187 (1999)
Druzin, M.L., Milton, H.J., Edersheim, T.G.: Relationship of baseline fetal heart rate to gestational age and fetal sex. American Journal of Obstetrics and Gynaecology 154, 1102–1103 (1986)
Jezewski, J., Wrobel, J., Horoba, K., Kupka, T., Matonia, A.: A Centralised fetal monitoring system with hardware-based data flow control. In: Proc. of III International Conference MEDSIP Glasgow, pp. 51–54 (2006)
Jezewski, M., Wrobel, J., Horoba, K., Gacek, A., Henzel, N., Leski, J.: The prediction of fetal outcome by applying neural network for evaluation of CTG records. In: Kurzynski, M., Puchala, E., et al. (eds.) Advances in Soft Computing Series, pp. 533–540. Springer, Heidelberg (2007)
Jezewski, M., Wrobel, J., Labaj, P., Leski, J., Henzel, N., Horoba, K., Jezewski, J.: Some practical remarks on neural networks approach to fetal cardiotocograms classification. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5170–5173 (2007)
Lange, S., Van Leeuwen, P., Geue, D., Hatzmann, W., Gronemeyer, D.: Influence of gestational age, heart rate, gender and time of day on fetal heart rate variability. Medical and Biological Engineering and Computing 43, 481–486 (2005)
Magenes, G., Signorini, M.G., Arduini, D.: Classification of cardiotocographic records by neural networks. In: Proc.of the IEEE International Joint Conference on Neural Networks, vol. 3, pp. 637–641 (2000)
Ogueh, O., Steer, P.: Gender does not affect fetal heart rate variation. British Journal of Obstetrics and Gynaecology, 1312–1314 (1998)
Sikora, J.: Digital analysis of cardiotocographic traces for clinical fetal outcome prediction. Clinical Perinatology and Gynaecology Supplement 21 (in polish, 2001)
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Jezewski, M., Czabanski, R., Horoba, K., Wrobel, J., Jezewski, J. (2008). Prediction of Newborn Sex with Neural Networks Approach to Fetal Cardiotocograms Classification. In: Pietka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Advances in Soft Computing, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68168-7_34
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DOI: https://doi.org/10.1007/978-3-540-68168-7_34
Publisher Name: Springer, Berlin, Heidelberg
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