Format

Send to

Choose Destination
Clin Neurophysiol. 2016 Jan;127(1):156-168. doi: 10.1016/j.clinph.2015.04.075. Epub 2015 May 9.

Validation of an automated seizure detection algorithm for term neonates.

Author information

1
Academic Research Department of Neonatology, Institute for Women's Health, University College London, London, United Kingdom.
2
Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
3
Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland. Electronic address: g.boylan@ucc.ie.

Abstract

OBJECTIVE:

The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres.

METHODS:

EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed.

RESULTS:

Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6-75.0%, with false detection (FD) rates of 0.04-0.36FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen's Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures.

CONCLUSION:

The SDA achieved promising performance and warrants further testing in a live clinical evaluation.

SIGNIFICANCE:

The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens.

KEYWORDS:

Automated seizure detection; Hypoxic-ischaemic encephalopathy; Neonatal EEG; Neonatal neurology; Neonatal seizures

PMID:
26055336
PMCID:
PMC4727504
DOI:
10.1016/j.clinph.2015.04.075
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center