A dataset of neonatal EEG recordings with seizure annotations

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Stevenson , N J , Tapani , K , Lauronen , L & Vanhatalo , S 2019 , ' A dataset of neonatal EEG recordings with seizure annotations ' , Scientific data , vol. 6 , 39 . https://doi.org/10.1038/sdata.2019.39

Title: A dataset of neonatal EEG recordings with seizure annotations
Author: Stevenson, N. J.; Tapani, K.; Lauronen, L.; Vanhatalo, S.
Contributor organization: HUS Medical Imaging Center
Department of Neurosciences
Kliinisen neurofysiologian yksikkö
Children's Hospital
University of Helsinki
HUS Children and Adolescents
Department of Diagnostics and Therapeutics
BioMag Laboratory
Date: 2019-03-05
Language: eng
Number of pages: 8
Belongs to series: Scientific data
ISSN: 2052-4463
DOI: https://doi.org/10.1038/sdata.2019.39
URI: http://hdl.handle.net/10138/300402
Abstract: Neonatal seizures are a common emergency in the neonatal intensive care unit (NICU). There are many questions yet to be answered regarding the temporal/spatial characteristics of seizures from different pathologies, response to medication, effects on neurodevelopment and optimal detection. The dataset presented in this descriptor contains EEG recordings from human neonates, the visual interpretation of the EEG by the human experts, supporting clinical data and codes to assist access. Multi-channel EEG was recorded from 79 term neonates admitted to the NICU at the Helsinki University Hospital. The median recording duration was 74 min (IQR: 64 to 96 min). The presence of seizures in the EEGs was annotated independently by three experts. An average of 460 seizures were annotated per expert in the dataset; 39 neonates had seizures and 22 were seizure free, by consensus. The dataset can be used as a reference set of neonatal seizures, in studies of inter-observer agreement and for the development of automated methods of seizure detection and other EEG analyses.
Subject: NUMBER
3112 Neurosciences
3124 Neurology and psychiatry
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

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