Source:http://linkedlifedata.com/resource/pubmed/id/17271611
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2007-2-2
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| pubmed:abstractText |
A data efficient blind sources separation (BSS) algorithm has been applied to preprocess intracranial EEG (ECoG) for artifact rejection. After artifacts correction a recurrence time statistics T1 feature was evaluated from the 'cleaned' data. Seizure detection performance was compared between BSS preprocessing and without preprocessing. Test results show that in a data set, for a detection rate of 96%, the false alarm rate dropped from 0.13 per hour without BSS preprocessing to 0.08 with preprocessing. For the other set of data, the false alarm rate dropped from 0.34 to 0.21 at a detection rate of 100%.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:issn |
1557-170X
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
1
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
91-4
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| pubmed:year |
2004
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| pubmed:articleTitle |
Evaluation of a BSS algorithm for artifacts rejection in epileptic seizure detection.
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| pubmed:affiliation |
Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA.
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| pubmed:publicationType |
Journal Article
|