Background electroencephalographic (EEG) activities of very preterm infants born at less than 27 weeks gestation: a study on the degree of continuity.
OBJECTIVE
To clarify the features of the background electroencephalographic (EEG) activities in clinically well preterm infants born at less than 27 weeks gestation and to outline their chronological changes with increasing postconceptional age (PCA).


METHODS
EEGs of clinically well premature infants born at less than 27 weeks gestation were recorded during the early postnatal period. The infants were separated into three groups according to their PCA at the time of EEG recording (21-22 weeks PCA, 23-24 weeks PCA, and 25-26 weeks PCA). The mean and maximum duration of interburst intervals (IBIs), the mean duration of bursts, and the percentage of continuous and discontinuous patterns in each PCA group were evaluated.


RESULTS
There were three infants at 21-22 weeks PCA, seven at 23-24 weeks PCA, and five at 25-26 weeks PCA. Eighteen EEG recordings were obtained. The mean and maximum IBI duration decreased with increasing PCA. The percentage of continuous patterns increased with increasing PCA. Conversely, the percentage of discontinuous patterns decreased with increasing PCA.


CONCLUSIONS
In premature infants born at less than 27 weeks gestation, the characteristics of the background EEG activities were similar to those of older premature infants. These changes reflect the development of the central nervous system in this period.
Hayakawa
M
M
Department of Pediatric Cardiology and Neonatology, Ogaki Municipal Hospital, Ogaki, Gifu-Pref, Japan. masahaya@med.nagoya-u.ac.jp
Okumura
A
A
Hayakawa
F
F
Watanabe
K
K
Ohshiro
M
M
Kato
Y
Y
Takahashi
R
R
Tauchi
N
N
D000704:Analysis of Variance; D001921:Brain / Q000254:growth & development; D002657:Child Development / Q000502:physiology; D003700:Delta Rhythm; D004569:Electroencephalography; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature / Q000502:physiology*; D018709:Statistics, Nonparametric