Publication
Title
Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing
Author
Abstract
Introduction In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. Materials The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonicclonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452 hours from 23 GAERS and 982 hours from 15 kainate-induced temporal-lobe epilepsy rats. Methods During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. Results A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonicclonic seizures achieved a BER of 16%. Conclusion Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG.
Language
English
Source (journal)
Artificial intelligence in medicine. - Tecklenburg
Publication
Tecklenburg : 2011
ISSN
0933-3657
Volume/pages
53:3(2011), p. 215-223
ISI
000296680600006
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 05.04.2012
Last edited 06.06.2017
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