Abstract
This paper describes several approaches to computer-supported recognition of accelerative and decelerative patterns in the Foetal Heart Rate signal with a view to automation of the diagnosis of foetal well being. The classifiers discussed evolve from a rule-based approach to a neuro-fuzzy system, via classical neural network architectures. The main problem regarding the symbolic approach is the limited possibility for knowledge elicitation. In order to resolve this problem and to obtain better limits for the decision regions, a classical neural network approach and a neuro-fuzzy system able to deal with ill-defined data were both investigated. The latter produced better results, with a classification rate of 72.6% for accelerations and 65.7% for decelerations. In the final section of our paper, we discuss how this approach opens up new lines of research, so as to improve the detection of accelerative and decelerative patterns.
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Fontenla-Romero, O., Guijarro-Berdiñas, B., Alonso-Betanzos, A. (2002). Symbolic, Neural and Neuro-fuzzy Approaches to Pattern Recognition in Cardiotocograms. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_34
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DOI: https://doi.org/10.1007/978-94-010-0324-7_34
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