Finnish researchers have developed a new software program based on machine learning, which can independently analyze EEG (electroencephalogram) signals from a premature infant to estimate the brain’s functional maturity.
The technique is the first EEG-based brain maturity evaluation system in the world and is more accurate than other methods currently used to measure infant brain development.
“This method gives us a first-time opportunity to track the most crucial development of a preterm infant, the functional maturation of the brain, both during and after intensive care,” says Professor Sampsa Vanhatalo from the University of Helsinki, who led the research.
Approximately one in ten newborns are born prematurely, and about half of all infants in neonatal intensive care are there because of preterm birth. Late pregnancy is a time of very rapid brain development for the fetus, and the brain’s electrical activity changes almost every week. The brain must be functioning correctly in order to develop correctly.
Preterm birth can significantly hinder brain development. Researchers found already in the 1980s that early health problems in preterm infants was often tied to slower brain development during the first months.
In order to provide the best possible care and develop new forms of treatment, it is important to know how the brain functions of infants develop, but no objective and precise methods for evaluating the early-stage maturity of the brain have been available.
One way to evaluate brain maturity is by placing EEG sensors on the scalp. This is a completely non-invasive, low-cost, and risk-free method, which has been very popular during the past few years in monitoring brain activity at neonatal intensive care units. But EEG alone poses some problems.
“The practical problem with EEG monitoring is that analysing the EEG data has been slow and required special expertise from the doctor performing it. This problem may be solved reliably and globally by using automatic analysis as part of the EEG device,” says Vanhatalo.
The new EEG analysis software was originally developed by Nathan Stevenson, an Australian engineer, who worked in Professor Vanhatalo’s research group as an EU-funded Marie Curie Fellow. The research used an extensive and well-controlled set of EEG measurement data from preterm infants.
The analysis software is based on machine learning. A large amount of EEG data on preterm babies was fed into a computer, and the software calculated hundreds of computational features from each measurement without intervention from a doctor. Then using an algorithm, these features were combined to generate a reliable estimate of the EEG maturational age of the infant.
Finally, the EEG maturational age estimated by the software was compared to the true age of the infant. In more than 80 percent of the cases, the true age of the infant and the computer-generated estimate fell within two weeks of one another.
The maturation estimate was so reliable and precise that in each of the 39 preterm infants in the study, the functional development of the brain could be tracked when the measurements were repeated every few weeks.
The study is published in the journal Scientific Reports.
Source: University of Helsinki