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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Published: 2023-12-30
DOI: https://doi.org/10.56294/dm2023113

Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks

By
Mohammed Moutaib ,
Mohammed Moutaib

IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco.

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Mohammed Fattah ,
Mohammed Fattah

L-STI, T-IDMS, University of Moulay Ismail of meknes, Faculty of Science and Technics, Errachidia, Morocco

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Yousef Farhaoui ,
Yousef Farhaoui

FS, My Ismail University, Meknes, Morocco

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Badraddine Aghoutane ,
Badraddine Aghoutane

IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

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Moulhime El Bekkali ,
Moulhime El Bekkali

IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco

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Abstract

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.

Keywords:

ECG,
CNN,
Fetal Electrocardiograms,
Machine Learning,
IoMT,

How to Cite

1.
Moutaib M, Fattah M, Farhaoui Y, Aghoutane B, El Bekkali M. Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks. Data and Metadata [Internet]. 2023 Dec. 30 [cited 2024 Jul. 3];2:113. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/113

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 2 (2023)
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