Machine Learning Algorithms Combining Slope Deceleration and Fetal Heart Rate Features to Predict Acidemia

Machine Learning Algorithms Combining Slope Deceleration and Fetal Heart Rate Features to Predict Acidemia

Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a...

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Bibliographic Details
Main Authors: Luis Mariano Esteban, Berta Castán, Javier Esteban-Escaño, Gerardo Sanz-Enguita, Antonio R. Laliena, Ana Cristina Lou-Mercadé, Marta Chóliz-Ezquerro, Sergio Castán, Ricardo Savirón-Cornudella
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Applied Sciences
Subjects:
electronic fetal monitoring
fetal heart rate
acidemia
machine learning
clinical utility curve
Online Access:https://www.mdpi.com/2076-3417/13/13/7478

Internet

https://www.mdpi.com/2076-3417/13/13/7478

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