Automatic burst detection for the EEG of the preterm infant

Ward Jennekens, Loes S. Ruijs, Charlotte M. L. Lommen, Hendrik J. Niemarkt, Jaco W. Pasman, Vivianne H. J. M. van Kranen-Mastenbroek, Pieter F. F. Wijn, Carola van Pul, Peter Andriessen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Web of Science)

Abstract

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLABR (R)) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% +/- 6%, 80% +/- 9% and 97% +/- 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% +/- 8%, 96% +/- 3% and 85% +/- 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.
Original languageEnglish
Pages (from-to)1623-1637
JournalPhysiological Measurement
Volume32
Issue number10
DOIs
Publication statusPublished - Oct 2011

Keywords

  • EEG
  • computer-assisted signal processing
  • premature infant

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