Evaluation of feature subsets for classification of cardiotocographic recordings

Electronic fetal monitoring - continuous recording of the cardiotocogram (CTG) - consisting of fetal heart rate (fHR) and tocographic signals, is a method used for intrapartal evaluation of the well being of the fetus. In this paper we evaluate several subsets of features for outcome classification of the pregnancy based on the CTG recording of the last 20 minutes preceding actual delivery. The features subsets are created based on PCA, information gain and Group of Adaptive Models Evolution (GAME) neural network feature selection algorithm. Our data set consisted of 104 intrapartum recordings including 18 pregnancies with acidemia reflected in umbilical artery pH<7.20. The results show that the best subset consisting of mix of time-domain and non-linear features performs consistently over the whole data set with sensitivity and specificity over 70%, which is well comparable to interobserver variations.