| EpiScan: Online seizure detection for epilepsy monitoring units. | |
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MedLine Citation:
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PMID: 22255730 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units. |
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Authors:
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Manfred M Hartmann; Franz Furbas; Hannes Perko; Ana Skupch; Katharina Lackmayer; Christoph Baumgartner; Tilmann Kluge |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Volume: 2011 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2011 Aug |
Date Detail:
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Created Date: 2012-01-18 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101243413 Medline TA: Conf Proc IEEE Eng Med Biol Soc Country: United States |
Other Details:
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Languages: eng Pagination: 6096-9 Citation Subset: IM |
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Descriptor/Qualifier:
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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