DocumentCode
1370572
Title
Extraction of spatio-temporal signatures from depth EEG seizure signals based on objective matching in warped vectorial observations
Author
Wendling, Fabrice ; Bellanger, Jean-Jacques ; Badier, Jean-Michel ; Coatrieux, Jean-Louis
Author_Institution
Lab. Traitement du Signal et de l´´Image, Rennes I Univ., France
Volume
43
Issue
10
fYear
1996
Firstpage
990
Lastpage
1000
Abstract
In the field of epilepsy, the analysis of stereoelectroencephalographic (SEEG) signals recorded with depth electrodes provides major information on interactions between brain structures during seizures. A comprehensive methodology of comparing SEEG seizure recordings is presented. It proceeds in three steps: 1) segmentation of SEEG signals; 2) characterization and labeling of segments; and 3) comparison of observations coded as sequences of symbol vectors. The third step reports a vectorial extension of the Wagner and Fischer´s algorithm to first, quantify similarities between observations and second, extract invariant sequences of events, referred to as spatio-temporal signatures. The study shows that two observations of nonequal duration can be matched by deforming the first one to optimally fit the second, under cost constraints. Results show that the methodology allows to exhibit signatures occurring during epileptic seizures and to point out different types of seizure patterns. The study brings objective results on reproducible interactions between brain structures during ictal periods and may help in the understanding of epileptogenic networks.
Keywords
electroencephalography; medical signal processing; sequences; vectors; Wagner-Fischer algorithm; brain structures; depth EEG seizure signals; electrodiagnostics; epileptic seizures; ictal periods; invariant event sequences; nonequal duration observations; objective matching; seizure patterns; signals segmentation; spatiotemporal signatures extraction; stereoelectroencephalographic signals analysis; vectorial extension; warped vectorial observations; Biomedical monitoring; Brain; Computed tomography; Diseases; Electrodes; Electroencephalography; Epilepsy; Magnetic resonance imaging; Neurons; Signal processing; Algorithms; Electroencephalography; Epilepsy, Temporal Lobe; Humans; Pattern Recognition, Automated; Reproducibility of Results; Seizures; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/10.536900
Filename
536900
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