Prediction of preterm labor from EHG signals using statistical and non-linear features

Prediction of preterm labor is of great importance to reduce neonatal death. Analysis of electrohysterogram (EHG) could be considered as a proper tool for this aim. In this paper, the statistical and non-linear features have been extracted from EHG signals and then Support Vector machine (SVM) has been applied for classification between term and preterm labor. The dataset of this research consists of 26 records from term delivery (duration of pregnancy ≥37 weeks) and 26 records from pre-term delivery (duration of pregnancy <37 weeks). The obtained results show the highest accuracy can be achieved by 4 statistical features from channel 1.