Identification of Fetal Sufferance Antepartum through a Multiparametric Analysis and a Support Vector Machine

The present work is concerned with the automatic identification of fetal sufferance in Intrauterine Growth Retarded (IUGR) fetuses, based on a multiparametric analysis of cardiotocographic recordings feeding a neural classifier. As classification tool, we propose a SVM (Support Vector Machine), which receives the set of linear and nonlinear parameters extracted from the fetal heart rate signal (FHR) as input and gives the indication of fetal distress as output. SVM is a powerful supervised learning algorithm belonging to the Statistical Learning Theory. It minimizes the structural risk performance in various classification problems. Three SVMs are built with different kernels. Their training set includes 70 cases: 35 normal and 35 IUGR suffering fetuses. Classification results obtained with a 2nd order polynomial kernel, on a test set of 30 unknown cases, show good values of accuracy, specificity and sensitivity. The SVM performance is very similar to that obtained with Multilayer Perceptron and Neurofuzzy classifiers proposed in previous works. The introduction of a hybrid unsupervised/supervised learning scheme integrating Independent Component Analysis (ICA) with SVM will be the natural development of this work with a further improvement of the diagnostic ability of the system.