Pregnancy Labor classification using neural network based analysis

Preterm labor (PL) is a major health issue worldwide. In this paper, we present a new framework to deal with this problem through processing of the electrohysterographic (EHG) signals which are recorded during labor and pregnancy. This new method is based on the neural network analysis to enhance the classification between labor and pregnancy. The proposed pipeline includes; i) using 16 EHG signals recorded from pregnant women’s abdomen; ii) Using network measures in order to characterize the connectivity matrices through graph-theory based analysis; iii) and classifying between labor and pregnancy contractions based on neural network methods. Results showed that the logistic regression method gives the best classification between pregnancy and labor. Neural network based analysis can be a beneficial tool to enhance the classification between labor and pregnancy EHG signals.

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