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

Palmu, K., Stevenson, N., Wikström, S., Hellström-Westas, L., Vanhatalo, S. and Palva, J. M. (2010) Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG. Physiological Measurement, 31(11), N85-N93. (doi:10.1088/0967-3334/31/11/n02) (PMID:20938065)

Palmu, K., Stevenson, N., Wikström, S., Hellström-Westas, L., Vanhatalo, S. and Palva, J. M. (2010) Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG. Physiological Measurement, 31(11), N85-N93. (doi:10.1088/0967-3334/31/11/n02) (PMID:20938065)

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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 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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Palva, Professor Matias
Authors: Palmu, K., Stevenson, N., Wikström, S., Hellström-Westas, L., Vanhatalo, S., and Palva, J. M.
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
Journal Name:Physiological Measurement
Publisher:IOP Publishing
ISSN:0967-3334
ISSN (Online):1361-6579

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