Optimal Electrohysterography Signal Preprocessing for Delivery Term Prediction Using Hypergraph Neural Networks

The robust prediction of the infant delivery term through the cooperation of artificial intelligence (AI) and electrohysterogram (EHG) would enable the appropriate early medication for possible premature delivery, thus avoiding death risk or sequels. This paper focuses on unraveling the best preprocessing scheme to be used when dealing with the classification of uterine muscular contraction signals. In addition, the study discusses the impact of several EHG denoising techniques on the prediction outcome. Hypergraph neural network (HGNN) is employed to evaluate the different preprocessing steps. The results show that it is better to start by segmenting the contractions and concatenating them, then standardizing the resulting signal or normalizing it between -1 and 1, and finally segmenting the contractions again in order to characterize them by a set of features that are used to finally train the classifier. The accuracy achieved by the HGNN given this methodology was 89.2%. Moreover, the use of the conventional EHG denoising methods such as canonical correlation analysis (CCA), empirical mode decomposition (EMD), and their combination (EMD-CCA) did not improve the prediction results. As a conclusion, proper preprocessing of the EHG signals would lead to a great improvement in the prediction process.Clinical Relevance- This study presents a new effective approach for preprocessing EHG signals to improve the prediction of delivery term.