Validation of an automated seizure detection algorithm for term neonates

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


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Electroencephalography / methods*
  • Female
  • Humans
  • Infant, Newborn
  • Male
  • Seizures / diagnosis*