Cardiotocograph Data Classification Improvement by Using Empirical Mode Decomposition.

This work proposes to study the fetal heart rate (FHR) signal based on information about its dynamics as a signal resulting from the modulation by the autonomic nervous system. The analysis is performed using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique. The main idea is to extract a set of signal features based on that technique and also conventional time-domain features proposed in the literature in order to study their performance by using a support vector machine (SVM) as a classifier.