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Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health.
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2010 (English)In: Physiological Measurement, ISSN 0967-3334, E-ISSN 1361-6579, Vol. 31, no 11, N85-N93 p.Article in journal (Refereed) Published
Abstract [en]

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.
Keyword [en]
EEG, preterm, SAT, burst, automated detection, NLEO
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-133715DOI: 10.1088/0967-3334/31/11/N02ISI: 000283491900002OAI: oai:DiVA.org:uu-133715DiVA: diva2:370980
Available from: 2010-11-18 Created: 2010-11-15 Last updated: 2017-12-12Bibliographically approved

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