Linked Life Data

8902366

Source:http://linkedlifedata.com/resource/pubmed/id/8902366

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
1997-2-5
pubmed:abstractText
This paper treats algorithms for feature extraction and clustering of multichannel EEG transients occurring in epilepsy, so called spikes. Hermite functions with a variable width parameter is used as features. We study nonlinear optimization of a series expansion for multichannel spikes. For the clustering problem, the nearest mean (NM) algorithm, generalized to matrix features, is used. The number of classes is assumed to be known a priori. The series expansion gives good signal description while reducing information. A simulation to estimate the space resolution capability of the algorithms indicates that perfect clustering requires approximately one head radius distance between the dipoles, which each generate one cluster. The NM algorithm was used to cluster two sets of clinically recorded spikes, and the clustering was compared to the manual clustering obtained by a neurophysiologist. For both spike sets evaluated, the clusters obtained by the algorithms had high accordance with the result of the neurophysiologist.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0010-4809
pubmed:author
pubmed:issnType
Print
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
382-94
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
Feature extraction and clustering of EEG epileptic spikes.
pubmed:affiliation
Department of Electrical Engineering and Computer Science, Lund University, Sweden.
pubmed:publicationType
Journal Article