Classification of Fetal State from the Cardiotocogram Recordings using ANN and Simple Logistic

Abstract

In this study, we present a comparison of machine learning technics using antepartum cardiotocographs performed by SisPorto 2.0 in predicting newborn outcome. CTG is widely used in pregnancy as a technique of measuring fetal well-being, mainly in pregnancies with increased risk of complications. It is a non-invasive way for checking the fetal conditions in the antepartum period. CTG is a continuous electronic record of the baby’s heart rate acquired via an ultrasound transducer placed on the mother’s abdomen. The information efficiently took out from these recordings can be used to envisage pathological state of the fetus and makes an early intervention possible before there is a permanent damage to the fetus. Using features extracted from the FHR and UC signals, the techniques ANN and Simple Logistic was trained to predict the normal and the pathological state. The dataset which consist of 1831 instances with 21 attributes was tested by using the methods which is mentioned above. The CTG recordings were also categorized 1655 of them as normal and 176 of them as pathological by three expert obstetricians’ consensus. They were showed that ANN and Simple Logistic based methods were able to classify the data as normal and pathological with 98.5% and 98.7% accuracy, respectively. Keywords: Cardiotocogram, CTG, SisPorto, Artificial Neural Network (ANN), Simple Logistic, feotus