Linked Life Data

20022319

Source:http://linkedlifedata.com/resource/pubmed/id/20022319

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-1-27
pubmed:abstractText
Approximately 30% of individuals with epilepsy have refractory seizures that cannot be controlled by current pharmacological treatment measures. For such patients, responsive neurostimulation prior to a seizure may lead to greater efficacy when compared with current treatments. In this paper, we present a real-time adaptive Wiener prediction algorithm implemented on a digital signal processor to be used with local field potential (LFP) recordings. The hardware implementation of the algorithm enables it to be a miniaturized portable system that could be used in a hand-held device. The adaptive nature of the algorithm allows the seizure data to be compared with baseline data occurring in the recent past rather than a preset value. This enhances the sensitivity of the algorithm by accounting for the time-varying dynamics of baseline, inter-ictal and ictal activity. The Wiener algorithm was compared to two statistical-based naïve prediction algorithms. ROC curves, area over ROC curves, predictive power, and time under false positives are computed to characterize the algorithm. Testing of the algorithm via offline Matlab analysis on kainate-treated rats results in prediction of seizures about 27 s before clinical onset, with 94% sensitivity and a false positive rate of 0.009 min(-1). When implemented on a real-time TI C6713 signal processor, the algorithm predicts seizures about 6.7s before their clinical onset, with 92% sensitivity and a false positive rate of 0.08 min(-1). These results compare favorably with those obtained in similar studies in terms of sensitivity and false positive rate.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1879-0534
pubmed:author
pubmed:copyrightInfo
2009 Elsevier Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
40
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
97-108
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Real-time seizure prediction from local field potentials using an adaptive Wiener algorithm.
pubmed:affiliation
Department of Biomedical Engineering, Purdue University, West Lafayette, IN, USA. poojarajdev@yahoo.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't