| Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing. | |
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MedLine Citation:
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PMID: 21955575 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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 tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours 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 tonic-clonic 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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Authors:
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Pieter Buteneers; David Verstraeten; Pieter van Mierlo; Tine Wyckhuys; Dirk Stroobandt; Robrecht Raedt; Hans Hallez; Benjamin Schrauwen |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-9-26 |
Journal Detail:
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Title: Artificial intelligence in medicine Volume: - ISSN: 1873-2860 ISO Abbreviation: - Publication Date: 2011 Sep |
Date Detail:
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Created Date: 2011-9-29 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8915031 Medline TA: Artif Intell Med Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011 Elsevier B.V. All rights reserved. |
Affiliation:
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Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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