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Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological
Insights
Heart rate variability (HRV) data can estimate functional maturational age (FMA) in infants using machine learning. This non-invasive method may help monitor infant development in neonatal intensive care units.
Area of Science:
- Neonatal physiology
- Biomedical engineering
- Machine learning in healthcare
Background:
- Assessing infant maturational status is crucial for neonatal care.
- Current methods may be invasive or time-consuming.
- Functional maturational age (FMA) deviation from postmenstrual age (PMA) indicates developmental progress.
Purpose of the Study:
- To evaluate the efficacy of heart rate variability (HRV) data for estimating infant FMA.
- To develop a machine learning model for non-invasive FMA assessment.
- To explore HRV, respiration rate variability (RRV), and bradycardia as potential biomarkers for maturation.
Main Methods:
- Acquired HRV data from 50 healthy infants (25-41 weeks gestational age).
- Utilized an ensemble machine learning (EML) model with feature selection (filtering, genetic algorithms).
- Validated the model using HRV, RRV, and bradycardia data.
Main Results:
- The EML model estimated FMA from HRV data with a mean absolute error of 0.93 weeks.
- Similar accuracy was achieved using RRV and bradycardia data.
- The model demonstrated the potential for real-time, non-invasive monitoring.
Conclusions:
- HRV, RRV, and bradycardia data can accurately estimate infant FMA.
- This approach offers a non-invasive, real-time method for monitoring neonatal development.
- The FMA deviation from PMA can serve as a valuable clinical indicator in neonatal intensive care units.
Abstract:
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 inform physicians about the progress of the maturation of the infants. The HRV data was acquired from 50 healthy infants, born between 25 and 41 weeks of gestational age, who did not present any signs of abnormal maturation relative to their age group during the period of observation. The HRV features were used as input for a machine learning model that uses filtering and genetic algorithms for feature selection, and an ensemble machine learning (EML) algorithm, which combines linear and random forest regressions, to produce as output a FMA. Using HRV data, the FMA had a mean absolute error of 0.93 weeks, 95% CI [0.78, 1.08], compared to the PMA. These results demonstrate that HRV features of newborn infants can be used by an EML model to estimate their FMA. This method was also generalized using respiration rate variability (RRV) and bradycardia data, obtaining similar results. The FMA, predicted either by HRV, RRV or bradycardia, and its deviation from the true PMA of the infants, could be used as a surrogate measure of the maturational age of the infants, which could potentially be monitored non-invasively and in real-time in the setting of neonatal intensive care units.
