| Automatic Seizure Detection Based on Star Graph Topological Indices. | |
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MedLine Citation:
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PMID: 22814089 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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The recognition of seizures is very important for the diagnosis of patients with epilepsy. The seizure is a process of rhythmic discharge in brain and occurs rarely and unpredictably. This behavior generates a need of an automatic detection of seizures by using the signals of long-term electroencephalographic (EEG) recordings. Due to the non-stationary character of EEG signals, the conventional methods of frequency analysis are not the best alternative to obtain good results in diagnostic purpose. The present work proposes a method of EEG signal analysis based on star graph topological indices (SGTIs) for the first time. The signal information, such as amplitude and time occurrence, is codified into invariant SGTIs which are the basis for the classification models that can discriminate the epileptic EEG records from the non-epileptic ones. The method with SGTIs and the simplest linear discriminant methods provide similar results to those previously published, which are based on the time-frequency analysis and artificial neural networks. Thus, this work proposes a simpler and faster alternative for automatic detection of seizures from the EEG recordings. |
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Authors:
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Enrique Fernandez-Blanco; Daniel Rivero; Juan Rabuñal; Julián Dorado; Alejandro Pazos; Cristian Robert Munteanu |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-7-16 |
Journal Detail:
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Title: Journal of neuroscience methods Volume: - ISSN: 1872-678X ISO Abbreviation: - Publication Date: 2012 Jul |
Date Detail:
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Created Date: 2012-7-20 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 7905558 Medline TA: J Neurosci Methods Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2012. Published by Elsevier B.V. |
Affiliation:
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Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, 15071, A Coruña, Spain. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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