Optimisation of an epileptiform activity detector for ambulatory use

Hdl Handle:
http://hdl.handle.net/10147/203612
Title:
Optimisation of an epileptiform activity detector for ambulatory use
Authors:
Thomas, E. M.; Kelleher, D.; Lightbody, G.; Nash, D.; McNamara, B.; Marnane, W. P.
Issue Date:
2010
URI:
http://hdl.handle.net/10147/203612
DOI:
10.1109/ITAB.2010.5687741
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5687741
Item Type:
Conference Presentation
Language:
en
Description:
A detector originally designed for seizure detection is modified to detect epileptiform activity in adults. The detector is intended for ambulatory use, and as such an emphasis is placed on the computational load of the detector. A framework is proposed making use of genetic algorithms in order to select the features for a Gaussian mixture classifier. Feature subset selection was performed by incorporating the computational load of each feature. This resulted in an improvement in classification results (larger area under both the ROC and PR curves), while reducing the runtime of the algorithm by up to 2000 fold with respect to a detector using the full feature set.

Full metadata record

DC FieldValue Language
dc.contributor.authorThomas, E. M.en
dc.contributor.authorKelleher, D.en
dc.contributor.authorLightbody, G.en
dc.contributor.authorNash, D.en
dc.contributor.authorMcNamara, B.en
dc.contributor.authorMarnane, W. P.en
dc.date.accessioned2012-01-18T15:59:22Z-
dc.date.available2012-01-18T15:59:22Z-
dc.date.issued2010-
dc.identifier.doi10.1109/ITAB.2010.5687741-
dc.identifier.urihttp://hdl.handle.net/10147/203612-
dc.descriptionA detector originally designed for seizure detection is modified to detect epileptiform activity in adults. The detector is intended for ambulatory use, and as such an emphasis is placed on the computational load of the detector. A framework is proposed making use of genetic algorithms in order to select the features for a Gaussian mixture classifier. Feature subset selection was performed by incorporating the computational load of each feature. This resulted in an improvement in classification results (larger area under both the ROC and PR curves), while reducing the runtime of the algorithm by up to 2000 fold with respect to a detector using the full feature set.en
dc.language.isoenen
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5687741en
dc.titleOptimisation of an epileptiform activity detector for ambulatory useen
dc.typeConference Presentationen
dc.description.provinceMunster-
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