Key result
Fuzzy clustering with pairs of prototypes improved the quality of automated fetal state assessment compared to fuzzy (c+p)-means clustering and Lagrangian Support Vector Machines.
Why the study?
Does fuzzy clustering with pairs of prototypes improve the automated assessment of fetal state from CTG signals compared to standard reference procedures?
Does fuzzy clustering with pairs of prototypes improve the automated assessment of fetal state from CTG signals compared to standard reference procedures?
Fuzzy clustering with pairs of prototypes improves the automated classification of fetal state from cardiotocographic signals.
May enhance benchmark CTG analysis; leaves open clinical validation and outcome impact.
Cardiotocographic (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.
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Jeżewski et al. (2016) studied Fetal state assessment. Fuzzy clustering with pairs of prototypes vs. Fuzzy (c+p)-means clustering and Lagrangian Support Vector Machines was evaluated on Quality of two- and three-class classification of fetal state. Fuzzy clustering with pairs of prototypes improved the quality of automated fetal state assessment compared to fuzzy (c+p)-means clustering and Lagrangian Support Vector Machines.
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