Quantitative analysis of amplitude-integrated electroencephalogram patterns in stable preterm infants, with normal neurological development at one year

H. J. Niemarkt, Peter Andriessen, Chris H.L. Peters, J. W. Pasman, C. E. Blanco, L. J. Zimmermann, S. Bambang Oetomo

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)

Abstract

Background: The amplitude-integrated EEG (aEEG) is feasible for monitoring cerebral activity in preterm infants. However, quantitative data on normal patterns in these infants are limited. Objective: To study maturational aEEG changes in a cohort of stable preterm infants by automated quantification. Methods: In a cohort of stable preterm infants with gestational age (GA) <32 weeks and normal neurological follow-up at 1 year, weekly 4 h EEG recordings were performed. aEEG traces were obtained from channel C3-C 4. The upper margin amplitude (UMA), lower margin amplitude (LMA) and bandwidth (BW) were quantitatively calculated using an expert software system. In addition, the relative duration of discontinuous background pattern (discontinuous background defined as activity with LMA <5 μV, expressed as DC-%) was calculated. Results: 79 aEEG recordings (4-6 recordings/infant) were obtained in 18 infants. Analysis of the first week recordings demonstrated a strong positive correlation between GA and LMA, while DC-% decreased significantly. Longitudinally, all infants showed increase of LMA. Multivariate analysis showed that GA and postnatal age (PA) both contributed independently and equally to LMA and DC-%. We found a strong correlation between postmenstrual age (GA + PA) and LMA and DC-%, respectively. Conclusion: To our knowledge, this is the first study where aEEG development was studied by automated quantification of aEEG characteristics in a cohort of stable preterm infants with a normal neurological development at 1 year of age. LMA and DC-% are simple quantitative measures of neurophysiologic development and may be used to evaluate neurodevelopment in infants.

Original languageEnglish
Pages (from-to)175-182
Number of pages8
JournalNeonatology
Volume97
Issue number2
DOIs
Publication statusPublished - 1 Feb 2010

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Premature Infants
Electroencephalography
Gestational Age
Expert Systems
Software
Multivariate Analysis

Keywords

  • Amplitude-integrated electroencephalography
  • Brain development
  • Electroencephalography
  • Preterm infants, amplitude-integrated EEG

Cite this

Niemarkt, H. J. ; Andriessen, Peter ; Peters, Chris H.L. ; Pasman, J. W. ; Blanco, C. E. ; Zimmermann, L. J. ; Bambang Oetomo, S. / Quantitative analysis of amplitude-integrated electroencephalogram patterns in stable preterm infants, with normal neurological development at one year. In: Neonatology. 2010 ; Vol. 97, No. 2. pp. 175-182.
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title = "Quantitative analysis of amplitude-integrated electroencephalogram patterns in stable preterm infants, with normal neurological development at one year",
abstract = "Background: The amplitude-integrated EEG (aEEG) is feasible for monitoring cerebral activity in preterm infants. However, quantitative data on normal patterns in these infants are limited. Objective: To study maturational aEEG changes in a cohort of stable preterm infants by automated quantification. Methods: In a cohort of stable preterm infants with gestational age (GA) <32 weeks and normal neurological follow-up at 1 year, weekly 4 h EEG recordings were performed. aEEG traces were obtained from channel C3-C 4. The upper margin amplitude (UMA), lower margin amplitude (LMA) and bandwidth (BW) were quantitatively calculated using an expert software system. In addition, the relative duration of discontinuous background pattern (discontinuous background defined as activity with LMA <5 μV, expressed as DC-{\%}) was calculated. Results: 79 aEEG recordings (4-6 recordings/infant) were obtained in 18 infants. Analysis of the first week recordings demonstrated a strong positive correlation between GA and LMA, while DC-{\%} decreased significantly. Longitudinally, all infants showed increase of LMA. Multivariate analysis showed that GA and postnatal age (PA) both contributed independently and equally to LMA and DC-{\%}. We found a strong correlation between postmenstrual age (GA + PA) and LMA and DC-{\%}, respectively. Conclusion: To our knowledge, this is the first study where aEEG development was studied by automated quantification of aEEG characteristics in a cohort of stable preterm infants with a normal neurological development at 1 year of age. LMA and DC-{\%} are simple quantitative measures of neurophysiologic development and may be used to evaluate neurodevelopment in infants.",
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Quantitative analysis of amplitude-integrated electroencephalogram patterns in stable preterm infants, with normal neurological development at one year. / Niemarkt, H. J.; Andriessen, Peter; Peters, Chris H.L.; Pasman, J. W.; Blanco, C. E.; Zimmermann, L. J.; Bambang Oetomo, S.

In: Neonatology, Vol. 97, No. 2, 01.02.2010, p. 175-182.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Quantitative analysis of amplitude-integrated electroencephalogram patterns in stable preterm infants, with normal neurological development at one year

AU - Niemarkt, H. J.

AU - Andriessen, Peter

AU - Peters, Chris H.L.

AU - Pasman, J. W.

AU - Blanco, C. E.

AU - Zimmermann, L. J.

AU - Bambang Oetomo, S.

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N2 - Background: The amplitude-integrated EEG (aEEG) is feasible for monitoring cerebral activity in preterm infants. However, quantitative data on normal patterns in these infants are limited. Objective: To study maturational aEEG changes in a cohort of stable preterm infants by automated quantification. Methods: In a cohort of stable preterm infants with gestational age (GA) <32 weeks and normal neurological follow-up at 1 year, weekly 4 h EEG recordings were performed. aEEG traces were obtained from channel C3-C 4. The upper margin amplitude (UMA), lower margin amplitude (LMA) and bandwidth (BW) were quantitatively calculated using an expert software system. In addition, the relative duration of discontinuous background pattern (discontinuous background defined as activity with LMA <5 μV, expressed as DC-%) was calculated. Results: 79 aEEG recordings (4-6 recordings/infant) were obtained in 18 infants. Analysis of the first week recordings demonstrated a strong positive correlation between GA and LMA, while DC-% decreased significantly. Longitudinally, all infants showed increase of LMA. Multivariate analysis showed that GA and postnatal age (PA) both contributed independently and equally to LMA and DC-%. We found a strong correlation between postmenstrual age (GA + PA) and LMA and DC-%, respectively. Conclusion: To our knowledge, this is the first study where aEEG development was studied by automated quantification of aEEG characteristics in a cohort of stable preterm infants with a normal neurological development at 1 year of age. LMA and DC-% are simple quantitative measures of neurophysiologic development and may be used to evaluate neurodevelopment in infants.

AB - Background: The amplitude-integrated EEG (aEEG) is feasible for monitoring cerebral activity in preterm infants. However, quantitative data on normal patterns in these infants are limited. Objective: To study maturational aEEG changes in a cohort of stable preterm infants by automated quantification. Methods: In a cohort of stable preterm infants with gestational age (GA) <32 weeks and normal neurological follow-up at 1 year, weekly 4 h EEG recordings were performed. aEEG traces were obtained from channel C3-C 4. The upper margin amplitude (UMA), lower margin amplitude (LMA) and bandwidth (BW) were quantitatively calculated using an expert software system. In addition, the relative duration of discontinuous background pattern (discontinuous background defined as activity with LMA <5 μV, expressed as DC-%) was calculated. Results: 79 aEEG recordings (4-6 recordings/infant) were obtained in 18 infants. Analysis of the first week recordings demonstrated a strong positive correlation between GA and LMA, while DC-% decreased significantly. Longitudinally, all infants showed increase of LMA. Multivariate analysis showed that GA and postnatal age (PA) both contributed independently and equally to LMA and DC-%. We found a strong correlation between postmenstrual age (GA + PA) and LMA and DC-%, respectively. Conclusion: To our knowledge, this is the first study where aEEG development was studied by automated quantification of aEEG characteristics in a cohort of stable preterm infants with a normal neurological development at 1 year of age. LMA and DC-% are simple quantitative measures of neurophysiologic development and may be used to evaluate neurodevelopment in infants.

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