Skip to content


  • Poster presentation
  • Open Access

The suppression curve as a new representation of the premature EEG maturation

  • 1, 2Email author,
  • 3,
  • 3,
  • 3,
  • 1, 2,
  • 4, 5,
  • 3 and
  • 1, 2
BMC Neuroscience201516 (Suppl 1) :P216

  • Published:


  • Neonatal Intensive Care Unit
  • Automate Analysis
  • Maturational Feature
  • Late Preterm
  • Suppression Curve
Automated analysis of premature electroencephalogram (EEG) for diagnosis is a crucial step to reduce the workload of neurologists. The grade of discontinuity gives important information about the maturation [1]. For normal maturation, the discontinuous pattern gradually evolves into a more continuous pattern. This means, interburst intervals (IBI), periods of low activity, become shorter. We have defined the suppression curve (SC), which is a "measure of discontinuity" [2] (Figure 1A). All data for this study were recorded at the Neonatal Intensive Care Unit, University Hospital Gasthuisberg, Leuven, Belgium. The dataset consisted of 170 EEG recordings (8 channels, 250 Hz) of 93 preterm infants with a postmenstrual age (PMA) of 24-40 weeks. Some maturational features are extracted from the discontinuous periods, like the IBI length and the synchrony index. However, the SC on itself gives also relevant information about the maturation. Taking the mean of every SC, we can find a correlation with the age till 34 weeks PMA (Figure 1B). Few outliers (abnormal EEG) are excluded. After that age, the patient is called late preterm or even term, and the EEG pattern is in normal condition mostly continuous (low values of the SC).
Figure 1
Figure 1

A Suppression curve example, containing 2 periods of 20-30 minutes of discontinuous pattern, B Evolution of the mean of the suppression curve in function of the age, · represents a patient with normal EEG, * patient with abnormal EEG

In conclusion, this research adds another valuable feature for the automated analysis of premature background EEG, which would improve the overall assessment in the NICU for EEG diagnosis



Research Council KUL: GOA/10/09 MaNet, CoE PFV/10/002 (OPTEC); PhD/Postdoc grants; Flemish Government: FWO, IWT: projects: TBM 110697-NeoGuard; PhD/Postdoc grants; Belgian Federal Science Policy Office: IUAP P7/19/ (DYSCO); EU: ERC Advanced Grant: BIOTENSORS (n° 339804).

Authors’ Affiliations

Division STADIUS, Department of Electrical Engineering (ESAT), University of Leuven, Leuven, Belgium
iMinds-KU Leuven Medical IT Department, Leuven, Belgium
Department of Development and Regeneration, University of Leuven, Leuven, Belgium
Department of Psychology, University of Oldenburg, Oldenburg, Germany
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK


  1. André M, Lamblin MD, d'Allest AM, Curzi-Dascalova L, Moussalli-Salefranque F, Nguyen S, et al: Electroencephalography in premature and full-term infants. Developmental features and glossary. Clin Neurophysiol. 2010, 40 (2): 59-124.View ArticleGoogle Scholar
  2. Koolen N, Jansen K, Vervisch J, Matic V, De Vos M, Naulaers G, Van Huffel S: Line length as a robust method to detect high-activity events: Automated burst detection in premature EEG recordings. Clin Neurophysiol. 2014, 125 (10): 1985-1994.PubMedView ArticleGoogle Scholar


© Koolen et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.