Abstract
Background: Continuous long-term multichannel EEG monitoring of the premature infant is feasible and could be a useful tool in early detection of pathological processes. This could help in early prediction of long-term outcome. To date interpretation and quantification of EEG have been based on visually edited recordings after rejection of artifacts. This is a cumbersome and time consuming process, specially in long-term monitoring. Thus a method for automated analysis is greatly needed.
Aim: To develop an algorithm for automated analysis of the EEG background activity of the continuous EEG from day one to three.
Method: 42 healthy infants with GA< 31 wk were included, monitored (NicoletOne monitor) continuously for 3 days soon after birth. 8 EEG electrodes were applied. The data were analyzed by visually removing artifacts (pruned), and by an algorithm removing the highest 5, 10, 15 or 20% of the total absolute bandpower (tABP).
Results are depicted in fig 1. (δ-tABP)
?emptypara?
There were good correlations between the pruned and the 4 different algorithms used, the 5% removal showing the closest relationship with 95 % match of the removed data.
Conclusion: This study shows a good relationship between pruned and automated analyses of the EEG recording.
Log in or create a free account to read this content
Gain free access to this article, as well as selected content from this journal and more on nature.com
or
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Schumacher, E., Westwik, Å., Larsson, P. et al. 104 Automated Analysis of Long-Term Continuous Multichannel Eeg in Premature Infants. Pediatr Res 68 (Suppl 1), 55–56 (2010). https://doi.org/10.1203/00006450-201011001-00104
Issue date:
DOI: https://doi.org/10.1203/00006450-201011001-00104
