Peer-Reviewed Journal Details
Mandatory Fields
Palmu, K,Stevenson, N,Wikstrom, S,Hellstrom-Westas, L,Vanhatalo, S,Palva, JM;
2010
January
Physiological Measurement
Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG
Validated
()
Optional Fields
EEG preterm SAT burst automated detection NLEO BRAIN PREMATURE ARTIFACTS INFANTS CORTEX BIRTH
31
85
93
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.
DOI 10.1088/0967-3334/31/11/N02
Grant Details