Fuzzy Analysis of Delivery Outcome Attributes for Improving the Automated Fetal State Assessment

Cardiotocography (CTG) is a standard procedure for fetal monitoring during pregnancy and labor. A number of automated methods for the classification of CTG recordings are based on supervised learning. Machine learning requires a set of parameters that quantitatively describe the acquired signals accompanied by a reference interpretation. This article presents a method of retrospective fetal-state assessment using the results of the fuzzy analysis of delivery outcome attributes. In real clinical datasets the class of signals related to an abnormal fetal state is usually underrepresented, which adversely affects the efficiency of the automated evaluation. Additionally, a method for reducing the disproportion between the class sizes based on the proposed fuzzy model is described. The fuzzy-inference-based learning of the Lagrangian Support Vector Machine (LSVM) increased the resulting efficiency of the fetal-state assessment.