Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine
The aim of this paper is to classify between Labor contractions and pregnancy contractions. Various types of parameters have been extracted from the electrohysterogram (EHG), mainly from the whole EHG or from different frequency bands. They have been computed from different signal databases obtained with different recording protocols. The results of these studies are sometime controversial. The aim of this paper is to compute 17 parameters selected from the literature on the same signal database, either on the whole EHG or after wavelet packet decomposition, and then to compare their power to discriminate between contractions recorded during pregnancy and labor. We thus obtain a selection of parameters that allow the best discrimination between pregnancy and labor contractions, when computed on the same signals, either on the whole EHG or on selected frequency bands. Index Terms Preterm labor, EHG, parameters extraction, Jeffrey divergence methods.