IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco.
L-STI, T-IDMS, University of Moulay Ismail of meknes, Faculty of Science and Technics, Errachidia, Morocco
FS, My Ismail University, Meknes, Morocco
IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco
IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco
Predicting fetal and maternal electrocardiograms (ECGs) is crucial in advanced prenatal monitoring. In this study, we explore the effectiveness of Convolutional Neural Networks (CNNs), using a carefully developed methodology to predict the category of fetal (F) or maternal (M) ECGs. In the first part, we trained a CNN model to predict fetal and maternal ECG images. In the following sections, the study results will be revealed. The CNN model demonstrated its ability to effectively discriminate between fetal and maternal patterns using automatically learned features.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.