Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG
2010 (English)In: Physiological Measurement, ISSN 0967-3334, E-ISSN 1361-6579, Vol. 31, no 11, N85-N93 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2010. Vol. 31, no 11, N85-N93 p.
EEG, preterm, SAT, burst, automated detection, NLEO
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:uu:diva-133715DOI: 10.1088/0967-3334/31/11/N02ISI: 000283491900002OAI: oai:DiVA.org:uu-133715DiVA: diva2:370980