PubMed Health⌕ Search

PubMed · 583442

Neonatal EEG and computerized tomography.

Abstract

The correlation between EEG and CT scan was studied in 57 full-term newborn infants with various neurological abnormalities, in order to clarify pathological processes underlying EEG abnormalities during the neonatal period. The relation between EEG and CT findings changes with the time elapsing after the acute phase of the perinatal brain insult. The same EEG pattern was associated with different CT findings of perinatal brain injury were usually associated with complete obliteration of cerebrospinal fluid spaces, a phenomenon considered to reflect severe brain swelling. Milkdly and minimally depressed EEGs in the acute phase were usually associated with localized decreased brain density, which is thought to represent localized edema. Moderately depressed background EEGs in the first week were associated with normal CT scans in most cases, although mild localized edema did not produce significant changes in the background EEG in some cases. In contrast to perinatal brain lesions, CT scans were not always well correlated with background EEGs in congenital cerebral dysplasia and intracranial hemorrhage, although the EEG was an useful adjunct to the CT scan in the diagnosis and prognostication of these disorders.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K Watanabe, S Miyazaki, K Hara, M Kuroyanagi, T Yamamoto, M Ito, S Nakamura, H Yamada. 1979. Neonatal EEG and computerized tomography.. https://doi.org/10.1055/s-0028-1085337

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A penalized likelihood approach to magnetic resonance image reconstruction.

Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.

Brain Diseases↗