Feasibility of automated early postnatal sleep staging in extremely and very preterm neonates using dual-channel EEG

Abstract

Objective: To investigate the feasibility of automated sleep staging based on quantitative analysis of dual-channel electroencephalography (EEG) for extremely and very preterm infants during their first postnatal days.

Methods: We enrolled 17 preterm neonates born between 25 and 30 weeks of gestational age. Three-hour behavioral sleep observations and simultaneous dual-channel EEG monitoring were conducted for each infant within their first 72 hours after birth. Four kinds of representative and complementary quantitative EEG (qEEG) metrics (i.e., bursting, synchrony, spectral power, and complexity) were calculated and compared between active sleep, quiet sleep, and wakefulness. All analyses were performed in offline mode.

Results: In separate comparison analyses, significant differences between sleep-wake states were found for bursting, spectral power and complexity features. The automated sleep-wake state classifier based on the combination of all qEEG features achieved a macro-averaged area under the curve of receiver operating characteristic of 74.8%. The complexity features contributed the most to sleep-wake state classification.

Conclusions: It is feasible to distinguish between sleep-wake states within the first 72 postnatal hours for extremely and very preterm infants using qEEG metrics.

Significance: Our findings offer the possibility of starting personalized care dependent on preterm infants' sleep-wake states directly after birth, potentially yielding long-run benefits for their developmental outcomes.

Keywords: Automated sleep staging; Complexity analysis; Early postnatal period; Preterm; Preterm sleep; Quantitative EEG.