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Methodology and system architecture for automated detection of epileptic seizures in the neonatal EEG
Glover, J.R. Ktonas, P.Y. Shastry, M. Thitai Kumar, A. Muktevi, V.M.
Dept. of Electr. & Comput. Eng., Houston Univ., TX, USA;

This paper appears in: [Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] EMBS/BMES Conference, 2002. Proceedings of the Second Joint
Publication Date: 2002
Volume: 1, On page(s): 70- 71 vol.1
ISSN: 1094-687X
ISBN: 0-7803-7612-9
INSPEC Accession Number: 7603880
Digital Object Identifier: 10.1109/IEMBS.2002.1134392
Current Version Published: 2003-01-06

Abstract
The automated detection of electrographic seizures in the neonatal EEG is a difficult, unsolved problem because of the variety of seizure patterns and the large number of seizure-like artifacts and non-seizure rhythmic EEG events. In this paper we present an architecture and methodology for such a detection system designed around a combination of signal processing, pattern recognition, heuristic rules, and neural networks. We believe that this hybrid approach offers the best chance for reliable automated detection of neonatal seizures.

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