期刊
PHYSIOLOGICAL MEASUREMENT
卷 31, 期 11, 页码 N85-N93出版社
IOP PUBLISHING LTD
DOI: 10.1088/0967-3334/31/11/N02
关键词
EEG; preterm; SAT; burst; automated detection; NLEO
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.
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