Quantitative analysis of maturational changes in EEG background activity in very preterm infants with a normal neurodevelopment at 1year of age

Niemarkt, H.J.; Andriessen, P.; Peters, C.H.L.; Pasman, J.W.; Zimmermann, L.J.; Bambang Oetomo, S.

doi:10.1016/j.earlhumdev.2010.03.003

« Previous Next »Early Human Development
Volume 86, Issue 4 , Pages 219-224, April 2010

Quantitative analysis of maturational changes in EEG background activity in very preterm infants with a normal neurodevelopment at 1year of age

H.J. Niemarkt
- Máxima Medical Centre, Neonatal Intensive Care Unit, 5500 MB Veldhoven, The Netherlands
P. Andriessen
- Máxima Medical Centre, Neonatal Intensive Care Unit, 5500 MB Veldhoven, The Netherlands
- Corresponding author. Máxima Medical Centre, Neonatal Intensive Care Unit, PO Box 7777, 5500 MB Veldhoven, The Netherlands.
C.H.L. Peters
- Máxima Medical Centre, Department of Clinical Physics, PO Box 7777, 5500 MB Veldhoven, The Netherlands
J.W. Pasman
- Department of Clinical Neurophysiology, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
L.J. Zimmermann
- Department of Pediatrics, Division of Neonatology, Maastricht University Medical Centre, 6229 HX Maastricht, The Netherlands
S. Bambang Oetomo
- Máxima Medical Centre, Neonatal Intensive Care Unit, 5500 MB Veldhoven, The Netherlands
- Department of Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands

Received 20 July 2009; received in revised form 14 January 2010; accepted 8 March 2010.

Abstract

Background

The electroencephalographic (EEG) background pattern of preterm infants changes with postmenstrual age (PMA) from discontinuous activity to continuous activity. However, changes in discontinuity have been investigated by visual analysis only.

Aim

To investigate the maturational changes in EEG discontinuity in healthy preterm infants using an automated EEG detection algorithm.

Study design

Weekly 4h EEG recordings were performed in preterm infants with a gestational age (GA)<32weeks and normal neurological follow-up at 1year. The channel C3–C4 was analyzed using an algorithm which automatically detects periods of EEG inactivity (interburst intervals). The interburst–burst ratio (IBR, percentage of EEG inactivity during a moving time window of 600s) and mean length of the interburst intervals were calculated. Using the IBR, discontinuous background activity (periods with high IBR) and continuous background activity (periods with low IBR) were automatically detected and their mean length during each recording was calculated. Data were analyzed with regression and multivariate analysis.

Results

79 recordings were performed in 18 infants. All recordings showed a cyclical pattern in EEG discontinuity. With advancing PMA, IBR (R²=0.64; p<0.001), interburst interval length (R²=0.43; p<0.001) and length of discontinuous activity (R²=0.38; p<0.001) decreased, while continuous activity increased (R²=0.50; p<0.001). Multivariate analysis showed that all EEG discontinuity parameters were equally influenced by GA and postnatal age.

Conclusion

Analyzing EEG background activity in preterm infants is feasible with an automated algorithm and shows maturational changes of several EEG derived parameters. The cyclical pattern in IBR suggests brain organisation in preterm infant.

Abbreviations: EEG, electroencephalogram, PMA, postmenstrual age, IBR, interburst burst ratio, GA, gestational age, PA, postnatal age, aEEG, amplitude-integrated EEG