Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG

Kirsi Palmu*, Nathan J. Stevenson, Sverre Wikström, Lena Hellström-Westas, Sampsa Vanhatalo, J. Matias Palva

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    39 Citations (Scopus)

    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 languageEnglish
    JournalPhysiological Measurement
    Volume31
    Issue number11
    DOIs
    Publication statusPublished - Nov 2010
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Automated detection
    • Burst
    • EEG
    • NLEO
    • Preterm
    • SAT

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