Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring.

Journal: Computer methods and programs in biomedicine
PMID: 31678794

Abstract

BACKGROUND AND OBJECTIVES: Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is a documented cause of fetal and neonatal morbidity and mortality. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. Therefore, designing an accurate model for the early and prompt identification of pathology in the antepartum period is crucial in view of pregnancy management.

Authors

  • Maria G Signorini
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. Electronic address: mariagabriella.signorini@polimi.it.
  • Nicolò Pini
    Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.
  • Alberto Malovini
    Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri, Pavia, Italy.
  • Riccardo Bellazzi
    Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Giovanni Magenes
    Dipartimento di Ingegneria Industriale e dell'Informazione, University of Pavia, 27100 Pavia, Italy. giovanni.magenes@unipv.it.

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