Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection

Karoliina T. Tapani*, Sampsa Vanhatalo, Nathan J. Stevenson

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

57 Citations (Scopus)
71 Downloads (Pure)


The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-frequency domain (time-frequency correlation; TFC). These measures were incorporated into a seizure detection algorithm (SDA) based on a support vector machine to detect periods of seizure and nonseizure. The performance of these nonstationary correlation measures was evaluated using EEG recordings from 79 term neonates annotated by three human experts. The proposed measures were highly discriminative for seizure detection (median AUCSC: 0.933 IQR: 0.821-0.975, median AUCTFC: 0.883 IQR: 0.707-0.931). The resultant SDA applied to multi-channel recordings had a median AUC of 0.988 (IQR: 0.931-0.998) when compared to consensus annotations, outperformed two state-of-the-art SDAs (p < 0.001) and was noninferior to the human expert for 73/79 of neonates.

Original languageEnglish
Article number1850030
Number of pages15
JournalInternational Journal of Neural Systems
Issue number4
Publication statusPublished - 1 May 2019
MoE publication typeA1 Journal article-refereed


  • Electroencephalography
  • neonatal seizure detection
  • nonstationary signal processing
  • support vector machines
  • time-frequency distributions


Dive into the research topics of 'Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection'. Together they form a unique fingerprint.

Cite this