Title
Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Tecklenburg ,
Subject
Human medicine
Computer. Automation
Source (journal)
Artificial intelligence in medicine. - Tecklenburg
Volume/pages
53(2011) :3 , p. 215-223
ISSN
0933-3657
ISI
000296680600006
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/fab016/911feda8dd7.pdf
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