title: Multi-channel amplitude-integrated EEG characteristics in preterm infants with a normal neurodevelopment at two years of corrected age.
abstract: OBJECTIVE
  To analyze quantitatively multi-channel amplitude-integrated EEG (aEEG) characteristics and assess regional differences.
  
  
  METHODS
  We investigated 40 preterm infants (postmenstrual age, PMA: range 27-37 weeks) with normal follow-up at 24 months of age, at a median postnatal age of 8 days using 4-h EEG recordings according to the international 10-20 system reduced montage. Nine (3 transverse and 6 longitudinal) channels were selected and converted to aEEG registrations. For each aEEG registration, lower margin amplitude (LMA), upper margin amplitude (UMA) and bandwidth (UMA-LMA) were calculated.
  
  
  RESULTS
  In all channels PMA and LMA showed strong positive correlations. Below 32 weeks of PMA, LMA was ≤5μV. Linear regression analysis showed a maximum LMA difference between channels of approximately 2 and 1μV at 27 and 37 weeks of PMA, respectively. The lowest are LMA values in the occipital channel and the highest values are in centro-occipital channels. In the frontal, centro-temporal and centro-occipital channels, UMA and bandwidth changed with PMA. No differences in LMA, UMA and bandwidth were found between hemispheres. Skewness of LMA values strongly correlated with PMA, positive skewness indicating an immature brain (PMA≤32 weeks) and negative skewness a maturing (PMA>32 weeks) brain.
  
  
  CONCLUSIONS
  We detected symmetric increase of aEEG characteristics, indicating symmetric brain maturation of the left and right hemispheres. Our findings demonstrate the clinical potential of computer-assisted analyses of aEEG recordings in detecting maturational features which are not readily identified visually. This may provide an objective and reproducible method for assessing brain maturation and long-term prognosis.
authors.affiliation: Máxima Medical Center, Veldhoven, The Netherlands.
authors.forename: Hendrik J
authors.initials: HJ
authors.lastname: Niemarkt
authors.forename: Ward
authors.initials: W
authors.lastname: Jennekens
authors.forename: Imke A
authors.initials: IA
authors.lastname: Maartens
authors.forename: Tessa
authors.initials: T
authors.lastname: Wassenberg
authors.forename: Marijke
authors.initials: M
authors.lastname: van Aken
authors.forename: Titia
authors.initials: T
authors.lastname: Katgert
authors.forename: Boris W
authors.initials: BW
authors.lastname: Kramer
authors.forename: Antonio W D
authors.initials: AW
authors.lastname: Gavilanes
authors.forename: Luc J
authors.initials: LJ
authors.lastname: Zimmermann
authors.forename: Sidarto
authors.initials: S
authors.lastname: Bambang Oetomo
authors.forename: Peter
authors.initials: P
authors.lastname: Andriessen
mesh_terms: D000367:Age Factors; D001921:Brain / Q000254:growth & development*; D002657:Child Development / Q000502:physiology*; D002675:Child, Preschool; D015331:Cohort Studies; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D005260:Female; D005865:Gestational Age; D057238:Growth Charts; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature / Q000254:growth & development* / Q000502:physiology; D008137:Longitudinal Studies; D008297:Male