title: The suppression curve as a quantitative approach for measuring brain maturation in preterm infants.
abstract: We apply the suppression curve (SC) as an automated approach to describe the maturational change in EEG discontinuity in preterm infants. This method allows to define normative values of interburst intervals (IBIs) at different postmenstrual ages (PMA).
  
  
  
  Ninety-two multichannel EEG recordings from 25 preterm infants (born ⩽32weeks) with normal developmental outcome at 9months, were first analysed using the Line Length method, an established method for burst detection. Subsequently, the SC was defined as the 'level of EEG discontinuity'. The mean and the standard deviation of the SC, as well as the IBIs from each recording were calculated and correlated with PMA.
  
  
  
  Over the course of development, there is a decrease in EEG discontinuity with a strong linear correlation between the mean SC and PMA till 34weeks. From 30weeks PMA, differences between discontinuous and continuous EEG become smaller, which is reflected by the decrease of the standard deviation of the SC. IBIs are found to have a significant correlation with PMA.
  
  
  
  Automated detection of individual maturational changes in EEG discontinuity is possible with the SC. These changes include more continuous tracing, less amplitude differences and shorter suppression periods, reflecting development of the vigilance states.
  
  
  
  The suppression curve facilitates automated assessment of EEG maturation. Clinical applicability is straight forward since values for IBIs according to PMA are generated automatically.
authors.affiliation: Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, University of Leuven (KU Leuven), Leuven, Belgium. Electronic address: anneleen.dereymaeker@uzleuven.be.
authors.forename: A
authors.initials: A
authors.lastname: Dereymaeker
authors.affiliation: Division STADIUS, Department of Electrical Engineering (ESAT), University of Leuven (KU Leuven), Leuven, Belgium; iMinds-KU Leuven Medical IT Department, Leuven, Belgium. Electronic address: ninah.koolen@esat.kuleuven.be.
authors.forename: N
authors.initials: N
authors.lastname: Koolen
authors.affiliation: Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, University of Leuven (KU Leuven), Leuven, Belgium; Department of Development and Regeneration, University Hospitals Leuven, Child Neurology, University of Leuven (KU Leuven), Belgium. Electronic address: katrien.jansen@uzleuven.be.
authors.forename: K
authors.initials: K
authors.lastname: Jansen
authors.affiliation: Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, University of Leuven (KU Leuven), Leuven, Belgium; Department of Development and Regeneration, University Hospitals Leuven, Child Neurology, University of Leuven (KU Leuven), Belgium. Electronic address: jan.vervisch@uzleuven.be.
authors.forename: J
authors.initials: J
authors.lastname: Vervisch
authors.affiliation: Department of Development and Regeneration, University Hospitals Leuven, Child Neurology, University of Leuven (KU Leuven), Belgium. Electronic address: els.ortibus@uzleuven.be.
authors.forename: E
authors.initials: E
authors.lastname: Ortibus
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK. Electronic address: maarten.devos@eng.ox.ac.uk.
authors.forename: M
authors.initials: M
authors.lastname: De Vos
authors.affiliation: Division STADIUS, Department of Electrical Engineering (ESAT), University of Leuven (KU Leuven), Leuven, Belgium; iMinds-KU Leuven Medical IT Department, Leuven, Belgium. Electronic address: sabine.vanhuffel@esat.kuleuven.be.
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
authors.affiliation: Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, University of Leuven (KU Leuven), Leuven, Belgium. Electronic address: gunnar.naulaers@uzleuven.be.
authors.forename: G
authors.initials: G
authors.lastname: Naulaers
mesh_terms: D000465:Algorithms; D001921:Brain / Q000254:growth & development / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D008297:Male
keywords: Automatic; Brain maturation; EEG; EEG discontinuity; Interburst intervals; Line length; Preterm infant; Suppression curve