Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological Signals

This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from the postmenstrual age (PMA) of the infants could...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics 2022-01, Vol.26 (1), p.400-410
Hauptverfasser:	Leon, Cristhyne, Cabon, Sandie, Patural, Hugues, Gascoin, Geraldine, Flamant, Cyril, Roue, Jean-Michel, Favrais, Geraldine, Beuchee, Alain, Pladys, Patrick, Carrault, Guy
Format:	Artikel
Sprache:	eng
Schlagworte:	Age Algorithms Bioengineering Bradycardia Cardiac arrhythmia Data acquisition Deviation Ensemble machine learning Feature extraction genetic algorithm Genetic algorithms Gestational Age Heart rate Heart Rate - physiology Heart rate variability Hospitals Humans Infant Infant, Newborn Infant, Premature - physiology Infants Intensive care units Learning algorithms Life Sciences Machine Learning Maturation Neonates Pediatrics Physicians Premature babies premature infants respiration rate Sociology Statistics
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