From Electrohysterogram Preview by a Deep Learning Method to Labor Prediction in Pregnant Women

Jossou TR, Tahori Z, Houdji G, Houessouvo RC and Et-Tahir A

Department of Acoustic Team-Biomedical Engineering, Mohammed V University of Rabat, Morocco
Ibn Tofail Kenitra University, Morocco
University of Abomey-Calavi, Benin

^*Correspondance to: Aziz Et-Tahir 

 PDF  Full Text Research Article | Open Access

Abstract:

The Electrohysterogram (EHG) is recognized as one of the best signatures of uterine contractions during gestation which has been used in the literature for the prediction of preterm births. The N-BEATS deep learning method has been applied to a dataset from an example of EHG by considering their first six principal components. Thus, it can be seen that the preview of EHG, and the early prediction of labor from the N-BEATS method, depends on the data size and recordings’ duration.

Keywords:

Principal component; Uterine contraction; Machine learning; Prediction

Cite the Article:

Jossou TR, Tahori Z, Houdji G, Houessouvo RC, Et-Tahir A. From Electrohysterogram Preview by a Deep Learning Method to Labor Prediction in Pregnant Women. Clin Case Rep Int. 2022; 6: 1265.