@article{schieder2026impact,
    type = {article},
    key = {schieder2026impact},
    author = {Laura Schieder and Nathalie Demme and Maha Shadaydeh and Claus Doerfel and Stella Jähkel and Joachim Denzler and Knut Holthoff and Hans Proquitté and Jürgen Graf},
    title = {Impact of Clinical Covariates on the Performance of an Automatic Sleep Stage Classification in Preterm Infants},
    journal = {Somnologie},
    year = {2026},
    url = {},
    code = {https://github.com/cvjena/SleepStageClassification},
    doi = {10.1007/s11818-026-00540-y},
    abstract = {Frequent sleep disruption in preterm infants in neonatal intensive care units (NICU) is suspected to be associated with adverse neurodevelopmental outcomes. A continuous, noninvasive sleep monitoring solution could optimise care by aligning interventions with natural sleep–wake cycles. The algorithm for automatic sleep stage classification by Demme et al. [5] offers a promising approach, with an accuracy of 92.2+-0.01% compared to sleep stages measured in the sleep laboratory.},
}