Automated neonatal seizure detection mimicking a human observer reading EEG

Abstract Objective The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant. Methods We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For e...

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Bibliographic Details
Published in:Clinical neurophysiology 2008-11, Vol.119 (11), p.2447-2454
Main Authors: Deburchgraeve, W, Cherian, P.J, De Vos, M, Swarte, R.M, Blok, J.H, Visser, G.H, Govaert, P, Van Huffel, S
Format: Article
Language:English
Subjects:
Algorithm
Algorithms
Biological and medical sciences
Case-Control Studies
Diagnosis, Computer-Assisted - methods
Electrodiagnosis. Electric activity recording
Electroencephalography (EEG)
Electroencephalography - methods
False Positive Reactions
Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy
Humans
Infant
Infant, Newborn
Infant, Newborn, Diseases - diagnosis
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Nervous system
Nervous system (semeiology, syndromes)
Neurology
Newborn
Predictive Value of Tests
Seizure detection
Seizures - classification
Seizures - diagnosis
Sensitivity and Specificity
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Summary:Abstract Objective The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant. Methods We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (
ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2008.07.281