Multiscale entropy analysis of combined EEG-fNIRS measurement in preterm
neonates
Abstract
In nature, biological systems such as the human brain are characterized
by complex and non-linear dynamics. One way of quantifying signal
complexity is Multiscale Entropy (MSE), which is suitable for structures
with long-range correlation at different time scales. In developmental
neuroscience, MSE can be taken as an index of brain maturation, and can
differentiate between healthy and pathological development. In our
current work, we explored the developmental trends of MSE on the basis
of 30 simultaneous EEG – fNIRS recordings in premature infants between
27 and 34 weeks of gestational age (wGA). To explore potential factors
impacting MSE, we determined the relation between MSE and the EEG Power
Spectrum Density (PSD) and Spontaneous Activity Transients (SATs). As a
result, via wGA, the MSE calculated on the EEG increases, thus
reflecting the maturational processes in the brain networks, whereas in
the fNIRS, MSE decreases, which might indicate a maturation of the brain
blood supply. Moreover, we propose that the EEG power in the beta band
(13 – 30 Hz) might be the main contributor to MSE in the EEG. Finally,
we highlight the importance of SATs in determining MSE as calculated
from the fNIRS recordings.