Detecting seizure origin using basic, multiscale population dynamic measures: preliminary findings.
Many types of electrographic seizures are readily identifiable by direct visual examination of electroencephalographic or electrocorticographic recordings. This process can, however, be painstakingly slow, and much effort has been expended to automate the process using various dynamic properties of epileptiform waveforms. As methods have become more subtle and powerful they have been used for seizure subclassification, seizure prediction, and seizure onset identification and localization. Here we concentrate on the last, with reference to seizures of neocortical origin. We briefly review some of the methods used and introduce preliminary results from a very simple dynamic model based on key electrophysiological properties found in some seizure types: occurrence of very fast oscillations (sometimes called ripples), excess gamma frequency oscillations, electroencephalographic/electrocorticographic flattening, and changes in global synchrony. We show how this multiscale analysis may reveal features unique to seizure onset and speculate on the underlying cellular and network phenomena responsible.
Roopun
A K
AK
Institute of Neuroscience, The Medical School, Newcastle University, Newcastle upon Tyne, UK.
Traub
R D
RD
Baldeweg
T
T
Cunningham
M O
MO
Whittaker
R G
RG
Trevelyan
A
A
Duncan
R
R
Russell
A J C
AJ
Whittington
M A
MA
D000818:Animals; D002648:Child; D002675:Child, Preschool; D003627:Data Interpretation, Statistical; D004569:Electroencephalography; D004828:Epilepsies, Partial / Q000503:physiopathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D051379:Mice; D012640:Seizures / Q000503:physiopathology*