Abstract
Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces is based on the visual analysis of patterns describing the variability of fetal heart rate signal. The correct interpretation of traces from a bedside monitor is rather difficult even for experienced clinicians, so computer-aided fetal monitoring systems have become very popular. At present effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The presented work describes an application the Artificial Neural Network Based on Logical Interpretation of fuzzy if-then Rules (ANBLIR) to classification of the fetal state as being normal or abnormal. A set of quantitative parameters describing fetal cardiotocograms is the system input. To evaluate the quality of the classification we proposed the overall validity index as a function of various prognostic indices. The obtained results confirm the usability of the ANBLIR neuro-fuzzy system for records classification within computer-aided fetal surveillance systems.
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References
Bezdek J C (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York
Czabanski R (2005) Neuro-fuzzy modeling based in a deterministic annealing approach. Int. J. Appl. Math. Comput. Sci. 15(4):561–576
Czabanski R (2006) Deterministic annealing integrated with ɛ-insensitive learning in neuro-fuzzy systems. Proc. Of 8th Int. Con. ICAISC, Zakopane 2006:220–229
Czabanski R (2006) Extraction of fuzzy rules using deterministic annealing integrated with ɛ-insensitive learning. Int. J. Appl. Math. Comput. Sci. 16(3):35–372
Czogała E, Leski J (1999) Fuzzy and neuro-fuzzy intelligent systems. Heidelberg: Physica-Verlag
Jezewski J, Wrobel J, Horoba K, Kupka T, Matonia A (2006) Centralized fetal monitoring system with hardware-based data flow control. Proc. Of III Int. Conf. MEDSIP, Glasgow 2006:51–54
Jezewski J, Wrobel J, Labaj P, Leski J, Henzel N, Horoba K, Jezewski J (2007) Some practical remarks on neural networks approach to fetal cardiotocograms classification. Proc. Of 29th Ann. Int. Conference of the IEEE/EMBS, Lyon 2007:5170–5173
Leski J (2003) Insensitive Learning Techniques for Approximate Reasoning Systems (Invited Paper). Int. J. of Computational Cognition 1:21–77
Rose K (1998) Deterministic annealing for clustering, compression, classification, regression and related optimization problems. Proc. IEEE 11:2210–2239
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© 2008 Springer-Verlag Berlin Heidelberg
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Czabanski, R., Jezewski, M., Wrobel, J., Horoba, K., Jezewski, J. (2008). A Neuro-Fuzzy Approach to the Classification of Fetal Cardiotocograms. In: Katashev, A., Dekhtyar, Y., Spigulis, J. (eds) 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics. IFMBE Proceedings, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69367-3_120
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DOI: https://doi.org/10.1007/978-3-540-69367-3_120
Publisher Name: Springer, Berlin, Heidelberg
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