Version 4.0, replaced annotations_2017_A.csv with annotations_2017_A_fixed.csv as there was extra data added to the first three columns of the annotations outside the duration of the EEG recording (annotations in all other files are correct). Thanks to Artur Gramacki for reporting the error. Version 3.0, corrected clinical_information.csv (in version 2.0 the sheet was missing 3 columns of data). Version 2.0, altered anonymous date and time in EDF header from 00.00.00 00.00.00 to 11.11.11 11.11.11 to allow reading by EDF Browser software. Added spreadsheet containing clinical report of neuro-imaging data and other demographics. Also, uploaded individual files rather than compressed zip file for easier access. Other Funding Organisations Finnish Academy: RIB - Rhythms in Infant Brain: Wearables for Computational Diagnostics and Mobile Monitoring of Treatment (314450) Finnish Academy: EBA - Early Brain Activity: Embedding Adversities in Neurodevelopment (288220) Juselius Foundation: Bridging the Birth Gap: From Fetal Stress and Early Nursing to Brain Network Development (BBG) Helsinki University Central Hospital: Digital Infant Neuromonitoring in hospitals and at home (DiVaNe) and Open Database for INfant EEG (ODIN)

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. This dataset contains EEG recordings from human neonates and the visual interpretation of the EEG by the human expert. Multi-channel EEG was recorded from 79 term neonates admitted to the neonatal intensive care unit (NICU) at the Helsinki University Hospital. The median recording duration was 74 minutes (IQR: 64 to 96 minutes). EEGs were annotated by three experts for the presence of seizures. An average of 460 seizures were annotated per expert in the dataset, 39 neonates had seizures by consensus and 22 were seizure free by consensus. The dataset can be used as a reference set of neonatal seizures, for the development of automated methods of seizure detection and other EEG analysis, as well as for the analysis of inter-observer agreement.

{"references": ["Tapani KT et al. Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection, Int J Neural Sys, 1850030 (2018)", "Stevenson NJ et al. The effect of reducing EEG electrode number on the visual interpretation of the human expert for neonatal seizure detection. Clin Neurophysiol 129, 265-270 (2018)"]}