Abstract
ABSTRACTCardiotocographic (CTG) monitoring, consisting in analysis of the fetal heart rate, uterine contractions, and fetal movements is the primary noninvasive method for the fetal state assessment. The visual interpretation of the CTG signals is characterized by the large inter- and intraobserver disagreement. Hence, the automated methods supporting the diagnosis process are the topic of researches. In the presented study, the evaluation of the CTG signals, based on fuzzy clustering with pairs of prototypes, is described. The efficiency of the proposed method is verified using two benchmark datasets of the CTG signals (CTU-UHB and SisPorto), and the problems of the two- and three-class classification are considered. The obtained results show the improved quality of the automated fetal state assessment in accordance with the applied reference procedures: the fuzzy (c+p)-means clustering and the Lagrangian Support Vector Machines.
Published Version
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