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Title
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Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Artificial intelligence in medicine. - Tecklenburg
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Publication
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Tecklenburg
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2011
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ISSN
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0933-3657
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DOI
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10.1016/J.ARTMED.2011.08.006
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Volume/pages
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53
:3
(2011)
, p. 215-223
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ISI
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000296680600006
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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