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J Neural Eng. 2012 Aug;9(4):046002. doi: 10.1088/1741-2560/9/4/046002. Epub 2012 Jun 19.

Inclusion of temporal priors for automated neonatal EEG classification.

Author information

1
Department of Electrical and Electronic Engineering, University College Cork, Ireland. andreyt@eleceng.ucc.ie

Abstract

The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 h. By exploiting these priors, the receiver operating characteristic area is increased by 23% (relative) reaching 96.74%. The number of false detections per hour is decreased from 0.45 to 0.25, while maintaining the correct detection of seizure burden at 70%.

PMID:
22713600
PMCID:
PMC3951711
DOI:
10.1088/1741-2560/9/4/046002
[Indexed for MEDLINE]
Free PMC Article

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