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Updated: Jun 8, 2026
Preterm EEG: A Multimodal Neurophysiological Protocol
Published on: February 18, 2012
Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm
Kirsi Palmu1, Nathan Stevenson, Sverre Wikström
1Department of Clinical Neurophysiology, University Hospital of Helsinki, Helsinki, Finland. kirsi.palmu@hus.fi
Abstract:
We propose here a simple algorithm for automated detection of spontaneous activity transients (SATs) in early preterm electroencephalography (EEG). The parameters of the algorithm were optimized by supervised learning using a gold standard created from visual classification data obtained from three human raters. The generalization performance of the algorithm was estimated by leave-one-out cross-validation. The mean sensitivity of the optimized algorithm was 97% (range 91-100%) and specificity 95% (76-100%). The optimized algorithm makes it possible to systematically study brain state fluctuations of preterm infants.
