Abstract
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.
Original language | English |
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Journal | Physiological Measurement |
Volume | 31 |
Issue number | 11 |
DOIs | |
Publication status | Published - Nov 2010 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Automated detection
- Burst
- EEG
- NLEO
- Preterm
- SAT