Space time frequency (STF) code tensor for the characterization of the epileptic preictal stage.
We evaluate the ability of multiway models to characterize the epileptic preictal period. The understanding of the characteristics of the period prior to the seizure onset is a decisive step towards the development of seizure prediction frameworks. Multiway models of EEG segments already demonstrated that hidden structures may be unveiled using tensor decomposition techniques. We propose a novel approach using a multiway model, Parallel Factor Analysis (PARAFAC), to identify spatial, temporal and spectral signatures of the preictal period. The results obtained, from a dataset of 4 patients, with a total of 30 seizures, suggest that a common structure may be involved in seizure generation. Furthermore, the spatial signature may be related to the ictal onset region and that determined frequency sub-bands may be more relevant in preictal stages.
Center for Informatics and Systems, University of Coimbra, 3030 Coimbra, Portugal. brunodireito@dei.uc.pt
Bruno
B
Direito
César
C
Teixeira
Bernardete
B
Ribeiro
Miguel
M
Castelo-Branco
António
A
Dourado
D000465:Algorithms; D016208:Databases, Factual; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D016018:Least-Squares Analysis; D008959:Models, Neurological; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors