Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effective...

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Bibliographic Details
Main Authors: Muhammad Omar Cheema, Zia Mohy-Ud-Din, Azhar Imran, Fawad Salam Khan, Mahmood Basil A. Al-Rawi, Mohammed A. El-Meligy, Jahan Zeb Gul
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Data augmentation
data enhancement
electrohysterogram
generative adversarial network
machine learning
term preterm
Online Access:https://ieeexplore.ieee.org/document/10974979/
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