The identification of electrographic seizures during long-term EEG monitoring in the neonate is currently based upon visual interpretation of the graphic record, a process that is very time-consuming. While significant progress has been made in the automated detection of seizures in the adult population, relatively little work has been done in the neonatal area. Therefore, the major objective of this project is the development of techniques for the reliable automated detection of electrographic seizures in the neonatal EEG. We propose a multi-stage, hybrid approach to detection that will employ a combination of signal processing, pattern recognition, neural networks, and expert rules. Through the successive stages of the detection process, multichannel neonatal EEG data containing all types of background activity and artifacts will be analyzed to detect and classify electrographic seizures. We postulate that the varied types and morphologies of seizures in the neonatal EEG, as compared to seizures in the adult EEG, can best be detected and classified using this hybrid approach. We will test the methods we develop on data recorded from infants in the Clinical Research Center for Neonatal Seizures, The Methodist Hospital, in Houston, Texas. We expect that the information we gain from the research will lead to the development of a practical seizure detection system and further our long-term goals of reduced expense in the reading and interpretation of neonatal EEGs, and also facilitate the efficient collection of seizure parameters for possible use in future research studies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS040577-02
Application #
6529026
Study Section
Special Emphasis Panel (ZRG1-BDCN-6 (01))
Program Officer
Fureman, Brandy E
Project Start
2001-09-15
Project End
2005-07-31
Budget Start
2002-08-01
Budget End
2003-07-31
Support Year
2
Fiscal Year
2002
Total Cost
$193,637
Indirect Cost
Name
University of Houston
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77204
Mitra, Joyeeta; Glover, John R; Ktonas, Periklis Y et al. (2009) A multistage system for the automated detection of epileptic seizures in neonatal electroencephalography. J Clin Neurophysiol 26:218-26
Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R et al. (2006) Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network. IEEE Trans Biomed Eng 53:633-41