Seizure detection of newborn EEG using a model-based approach

Seizures are often the first sign of neurological disease or dysfunction in the newborn. However, their clinical manifestation is often subtle, which tends to hinder their diagnosis at the earliest possible time. This represents an undesirable situation since the failure to quickly and accurately di...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 1998-06, Vol.45 (6), p.673-685
Main Authors: Roessgen, M., Zoubir, A.M., Boashash, B.
Format: Article
Language:English
Subjects:
Algorithms
Australia
Biological and medical sciences
Biomedical signal processing
Biophysics
Brain injuries
Brain modeling
Delivery. Postpartum. Lactation
Diseases
Disorders
Electroencephalography
Epilepsy
Gynecology. Andrology. Obstetrics
Humans
Infant, Newborn
Infant, Premature, Diseases - diagnosis
Likelihood Functions
Maximum likelihood detection
Medical sciences
Models, Neurological
Models, Statistical
Pediatrics
Seizures - diagnosis
Signal Processing, Computer-Assisted
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Summary:Seizures are often the first sign of neurological disease or dysfunction in the newborn. However, their clinical manifestation is often subtle, which tends to hinder their diagnosis at the earliest possible time. This represents an undesirable situation since the failure to quickly and accurately diagnose seizure can lead to longer-term brain injury or even death. Here, the authors consider the problem of automatic seizure detection in the neonate based on electroencephalogram (EEG) data. They propose a new approach based on a model for the generation of the EEG, which is derived from the histology and biophysics of a localized portion of the brain. They show that by using this approach, good detection performance of electrographic seizure is possible. The model for seizure is first presented along with an estimator for the model parameters. Then the authors present a seizure-detection scheme based on the model parameter estimates. This scheme is compared with the quadratic detection filter (QDF), and is shown to give superior performance over the latter. This is due to the ability of the model-based detector to account for the variability (nonstationarity) of the EEG by adjusting its parameters appropriately.
ISSN:0018-9294
1558-2531
DOI:10.1109/10.678601