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

doi:10.1088/0967-3334/31/11/N02

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 journal › Article › Scientific › peer-review

48 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 language	English
Journal	Physiological Measurement
Volume	31
Issue number	11
DOIs	https://doi.org/10.1088/0967-3334/31/11/N02
Publication status	Published - Nov 2010
MoE publication type	A1 Journal article-refereed

Keywords

Automated detection
Burst
EEG
NLEO
Preterm
SAT

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10.1088/0967-3334/31/11/N02

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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.",

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AU - Wikström, Sverre

AU - Hellström-Westas, Lena

AU - Vanhatalo, Sampsa

AU - Palva, J. Matias

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AB - 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.

KW - Automated detection

KW - Burst

KW - EEG

KW - NLEO

KW - Preterm

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

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Keywords

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