| Low complexity algorithm for seizure prediction using Adaboost. | |
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MedLine Citation:
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PMID: 23366078 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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This paper presents a novel low-complexity patient-specific algorithm for seizure prediction. Adaboost algorithm is used in two stages of the algorithm: feature selection and classification. The algorithm extracts spectral power features in 9 different sub-bands from the electroencephalogram (EEG) recordings. We have proposed a new feature ranking method to rank the features. The key (top ranked) features are used to make a prediction on the seizure event. Further, to reduce the complexity of classification stage, a non-linear classifier is built based on the Adaboost algorithm using decision stumps (linear classifier) as the base classifier. The proposed algorithm achieves a sensitivity of 94.375% for a total of 71 seizure events with a low false alarm rate of 0.13 per hour and 6.5% of time spent in false alarms using an average of 5 features for the Freiburg database. The low computational complexity of the proposed algorithm makes it suitable for an implantable device. |
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Authors:
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Manohar Ayinala; Keshab K Parhi |
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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: 2012 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2012 Aug |
Date Detail:
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Created Date: 2013-01-31 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: 1061-4 Citation Subset: IM |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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