Abstrakti
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 leaveone-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.
Alkuperäiskieli | Englanti |
---|---|
Julkaisu | Physiological Measurement |
Vuosikerta | 31 |
Numero | 11 |
DOI - pysyväislinkit | |
Tila | Julkaistu - marrask. 2010 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |