Title :
Methodology and system architecture for automated detection of epileptic seizures in the neonatal EEG
Author :
Glover, John R. ; Ktonas, Periklis Y. ; Shastry, Mruthyunjaya ; Kumar, Arun Thitai ; Muktevi, Venu
Author_Institution :
Dept. of Electr. & Comput. Eng., Houston Univ., TX, USA
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.
Keywords :
diseases; electroencephalography; medical signal detection; neural nets; paediatrics; patient monitoring; automated detection; electrodiagnostics; epileptic seizures; graphic record; heuristic rules; long-term EEG monitoring; neonatal EEG; pattern recognition; reliable automated detection; seizure detection; system architecture; visual interpretation; Computer architecture; Electroencephalography; Epilepsy; Event detection; Frequency; Intelligent networks; Pattern recognition; Pediatrics; Signal processing; Spectral analysis;
Conference_Titel :
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
Print_ISBN :
0-7803-7612-9
DOI :
10.1109/IEMBS.2002.1134392