Fuzzy system for evaluation of fetal heart rate signals using FIGO criteria

• Autorzy: Czabański R. , Jeżewski M. , Wróbel J. , Jeżewski J. , Horoba K.
• Języki publikacji: EN

Abstrakty

EN

Cardiotocography is a biophysical method of fetal monitoring during pregnancy and labour. It is mainly based on recording and analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the recorded signals but the effective methods supporting the conclusion generation are still needed. The evaluation of the signal can be made using criteria recommended by FIGO. Nevertheless, the quantitative description of the traces is inconsistent with qualitative nature of the obstetric knowledge. Therefore, we applied the fuzzy system based on Takagi-Sugeno-Kang model to evaluate and classify signals. FIGO guidelines were used for developing a set of fuzzy conditional rules defining the system performance. The proposed system was evaluated using data collected with computerized fetal surveillance system – MONAKO. The classification results confirm the improvement of the fetal state evaluation quality while using the proposed fuzzy system support.

Słowa kluczowe

EN

fetal monitoring; fetal heart rate; signal classification; fuzzy systems

PL

monitoring płodu; tętno płodu; kryteria FIGO

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2009
• Tom: Vol. 13
• Strony: 189--194
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Czabański R.

• Silesian University of Technology, Institute of Electronics, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Jeżewski M.

autor

Wróbel J.

autor

Jeżewski J.

autor

Horoba K.

Bibliografia

• [1] CZABANSKI R., JEZEWSKI M., WROBEL J., KUPKA T., LESKI J., JEZEWSKI J., The prediction of the low fetal birth weight based on quantitative description of cardiotocographic signals. Journal of Medical Informatics and Technologies, vol.12, pp.97–102, 2008.
• [2] FIGO News: Guidelines for the use of fetal monitoring. Int. J. Gynecol. Obstet. Vol.25, pp.159-167, 1987.
• [3] FISCHER W.M., STUDE I., BRANDT H., Ein Vorschlag zur Beurteilung des antepartalen Kardiotokogramms, Geburtshilfe und Perinatologie. Vol.180, No.2, pp.117-223, 1976
• [4] JEZEWSKI J., WROBEL J., HOROBA K., KUPKA T., MATONIA A., Centralised fetal monitoring system with hardware-based data flow control, Proc. of III Int. Conf. MEDSIP, pp.51–54, Glasgow, 2006.
• [5] KOL S., THALER I., PAZ N., SHMUELI O., Interpretation of nonstress test by an artificial neural networks, American Journal of Obstetrics & Gynecology, Vol.172, No.5, pp.1372-1378, 1995.
• [6] MAGENES G., SIGNORINI M.G., ARDUINI D., Classification of cardiotocographic records by neural networks, Proc. of the IEEE Int. Joint Conf. on Neural Networks, Vol.3, pp.637–641, 2000.
• [7] STRASZECKA E., Combining uncertainty and imprecision in models of medical diagnosis, Information Sciences, Vol.176, No.20, pp.3026-3059, 2006.
• [8] STREET P., DAWES G.S., MOULDEN M., REDMAN C.W., Short-term variation in abnormal antenatal fetal rate records. Am J. Obstet. Gynecol. Vol.165, No.3, pp.515–523, 1991.
• [9] SUGENO M., KANG G.T., Structure identification of fuzzy model. Fuzzy Sets and Systems. Vol.28, No.1, pp.15-33, 1988.
• [10] TAKAGI T., SUGENO M., Fuzzy identification of systems and its application to modeling and control. IEEE Trans. System Man and Cybernetics, Vo.15, No.1, pp.116-132, 1985.
• [11] ZADEH L.A., Fuzzy sets. Information and Control, Vol.8, No.4, pp.338–353, 1965.