Abstract
Cardiotocographic monitoring based on analysis of fetal heart rate, uterine contractions and fetal movements is a primary method for diagnosis of fetal state and prediction of fetal outcome. Visual assessment of signals is very difficult and characterized by intraobserver and interobserver disagreement. In the presented paper, a fuzzy clustering method was applied to cardiotocographic signals classification for fetal outcome prediction. The classifier’s fuzzy if-then rules are created based on obtained prototypes. A cross-validation procedure using 100 pairs of learning and testing subsets was applied to validate the results. The obtained results (classification error equal to 21% and sensitivity index equal to 76%) were better in comparison to the Lagrangian SVM method, which is modified version of the best known classification algorithms—Support Vector Machines.
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Jeżewski, M., Łęski, J. (2011). An Application of Fuzzy Clustering Method to Cardiotocographic Signals Classification. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_34
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DOI: https://doi.org/10.1007/978-3-642-23169-8_34
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